neuron-request@HPLMS2.HPL.HP.COM ("Neuron-Digest Moderator Peter Marvit") (05/26/90)
Neuron Digest Friday, 25 May 1990 Volume 6 : Issue 35 Today's Topics: ISSNNet meeting/dinner at IJCNN Korean Neural Network Activities Re: Intel N64 Re: Intel N64 Connections among nodes within a layer Re: Connections among nodes within a layer Re: Connections among nodes within a layer Re: Connections among nodes within a layer Re: Connections among nodes within a layer Is there any quantitative measure of accuracy of a bp network? Back Propagation for Training Every Input Pattern with Multiple Output Re: Back Propagation for Training Every Input Pattern with Multiple Output servo systems Re: servo systems feature extraction in pictures (In English) Back-propagation/NN benchmarks Re: Back-propagation/NN benchmarks Re: Back-propagation/NN benchmarks Re: Back-propagation/NN benchmarks Send submissions, questions, address maintenance and requests for old issues to "neuron-request@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request" Use "ftp" to get old issues from hplpm.hpl.hp.com (15.255.176.205). ------------------------------------------------------------ Subject: ISSNNet meeting/dinner at IJCNN From: issnnet@bucasb.bu.edu Date: Fri, 25 May 90 13:29:35 -0400 The International Student Society for Neural Networks (ISSNNet) will hold a meeting on Monday night, June 18, during the IJCNN conference in San Diego. All interested parties are welcome to join us. We are planning to organize a (cheap), quick meal right before or after the meeting, so participants may attend the evening plenary talks. We also expect to get a lot of people together after the plenaries and head over to some local establishment (you do not need to be a member to join us there :-). Exact details will be available at registration or at the ISSNNet booth during the conference. For more information send email to: issnnet@bucasb.bu.edu ------------------------------ Subject: Korean Neural Network Activities From: Soo Young Lee <sylee%eekaist.kaist.ac.kr@RELAY.CS.NET> Date: Fri, 25 May 90 11:40:42 -0700 [[ Editor's Note: I am always pleased to have reports of activities from different labs and (!) countries. I hope readers of this Digest will take advantage of Soo-Young's offer to act as intermediary between Korean researchers and Neuron Digest readers! For for my own records, I usually ask new subscribers what they do. However, I'd like to invite *current* subscribers to submit a short description of their own or groups' projects to be published in future Digests. I'm sure the general readership will be interested in what *you* have to say and what *you* are doing. -PM ]] Dear Moderator: I have some information about neural network activities in Korea. Please distribute followings for the Neuron Digest group. Thank you in advance. Soo-Young Lee KAIST - ----------------------------------------------------------------- Subject: Annual Meeting of Neural Network Study Group in Korea From: sylee%eekaist.kaist.ac.kr@relay.cs.net Date: May 24th, 1990 On May 19th, 1990, there was a big event on neural networks in Korea, ANNUAL MEETING of Neural Network Study Group (NNSG) of Korea. The meeting was also supported by Neural Network Committee of Korean Institute of Communication Sciences (KICS), Special Interest Group - Artificial Intelligence of Korean Information Science Society (KISS), and Special Interest Group - Korea of International Neural Network Society (SIGINNS-Korea). Book of summaries of the technical presentations is available by request to: sylee%eekaist.kaist.ac.kr@relay.cs.net or Prof. Soo-Young Lee Dept. of Electrical Engineering Korea Advanced Institute of Science and Technology P.O.Box 150 Chongryangni Seoul, 130-650 Korea. Also if you want information on Korean neural network activities, NNSG of Korea, Neural Network Committee of KICS, or SIGINNS-Korea, I may provide that for you. As a Secretary of NNSG, Chairman of Neural Network Committee of KICS, and also Co-chairman of SIGINNS-Korea, I voluntereed to serve as a point-of-contact between Korean neural network researchers and Neuron Digest group. Best regards, Soo-Young Lee FOREWORD of the Digest Neural Network Study Group (NNSG) was established on August, 1988, and has organized regular seminars bimonthly to provide a forum for information exchange among neural network researchers and to promote research environment in Korea. The NNSG initially started with about 30 partici- pants, but now has more than 80 professors and industry researchers in the distribution list. Also we understand there are many other neural network researchers in Korea who may join the NNSG in the near furture. As an attempt to summarize all the neural network research activities in Korea, an ANNUAL MEETING is organized by the NNSG in coor- poration with Neural Network Committee of Korean Institute of Communication Sciences, Special Interest Group - Artificial Intelligence of Korean Information Science Society, and Spe- cial Interest Group - Korea of International Neural Network Society (SIGINNS-Korea). Twenty eight papers are presented at the Technical Presentation Sessions. Although only summaries are included in this digest, we hope it gives you some ideas of on-going neural network research activities in Korea. May 19th, 1990 Secretaries Neural Network Study Group TABLE OF CONTENTS SESSION I Theory 13:00-13:20 Computation of Average Vector in Primate Saccadic Eye Movement System Choonkil Lee, Kyung-Hee Yoon, and Kye-Hyun Kyung, Seoul National University 13:20-13:40 Computer Simulation of Central Pattern Generation in Invertebrates Jisoon Ihm, Seoul National University 13:40-14:00 Harmony Theory Koo-Chul Lee, Seoul National University 14:00-14:20 Hamiltonian of the Willshaw Model with Local Inhibition Gyoung Moo Shim and Doochul Kim, Seoul National University 14:20-14:40 Modular Neural Networks: Combining the Coulomb Energy Network Algorithm and the Error Back Propagation Algorithm Won Don Lee, Chungnam National University 14:40-15:00 Dynamic Model of Neural Networks M.Y. Choi, Seoul National University SESSION II Application (I) 13:00-13:20 Neural Network Control for Robotics and Automation Se-Young Oh, Pohang Institute of Science and Technology 13:20-13:40 A Modified Hopfield Network: Design, Application and Implementation Chong Ho Lee, Sung Soo Dong, Inha University Kye Hyun Kim, Bo Yeon Kim, Seoul National University 13:40-14:00 Frequency Analysis of Speech Signal using Laplacian of Gaussian Operator Yang-Sung Lee, Jae-Chang Kim, Ui-Yul Park, Tae-Hoon Yoon, Yong-Guen Jung, Pusan National University, and Jong-Hyek Lee, Kyungsung University 14:00-14:20 Korea Phoneme Classification using the Modified LVQ2 Algorithm Hong K. Kim, Hwang S. Lee, Soo Y. Lee, and Chong K. Un, Korea Advanced Institute of Science and Technology 14:20-14:40 User Authentification by a Keyboard Typing Patterns using a Neural Net Algorithm Jaeha Kim, Joonho Lee, and Kangsuk Lee, Samsung Advanced Institute of Technology 14:40-15:00 An Artificial Neural Net Approach to Time Series Modeling: ARMA Model Identification Sung Joo Park and Jin Seol Yang, Korea Advanced Institute of Science and Technology SESSION III Implementation 15:20-15:40 Neural Networks on Parallel Computers Hyunsoo Yoon and Seung R. Maeng Korea Advanced Institute of Science and Technology 15:40-16:00 Automatic Design of Neural Circuits by the Single Layer Perceptron and Unidirectional Type Models Ho Sun Chung, Kyungpook National University 16:00-16:20 Design of Neural Chip for Conversion of Binary Dither Image to Multilevel Image Jin Kyung Ryeu, Kyungpook National University 16:20-16:40 The Design of Neural Circuits for the Preprocessing of the Character Recognition Ji-Hwan Ryeo, Taegu University 16:40-17:00 Optical Associative Memory Based on Inner Product Neural Network Model Jong-Tae Ihm, Sang-Keun Gil, Han-Kyu Park, Yonsei University Seung-Woo Chung, ETRI, and Ran-Sook Kim, KTA 17:00-17:20 Optical Holographic Heteroassociative Memory System Seung-Hyun Lee, Woo-Sang Lee, and Eun-Soo Kim, Kwangwoon University 17:20-17:40 Optical Implementation of Multilayer Neural Networks Sang-Yung Shin, Korea Advanced Institute of Science and Technology 17:40-18:00 Re-training Neural Networks for Slightly Modified Input Patterns and its Optical Implementation for Large Number of Neurons Soo-Young Lee, Korea Advanced Institute of Science and Technology SESSION IV Application (II) 15:20-15:40 Efficient Image Labeling Using the Hopfield Net and Markov Random Field Hyun S. Yang, Korea Advanced Institute of Science and Technology 15:40-16:00 On the Improvements for Applying Neural Network to Real World Problems Sung-Bae Cho and Jin H. Kim, Korea Advanced Institute of Science and Technology 16:00-16:20 An Image Reinforcement Algorithm,ALOPEX, Conducted on Connectivity by Using Moment Invarient as a Feature Extraction Method Tae-Soo Chon, Pusan national University, and Evangelia Tzanakou, Rutgers University 16:20-16:40 Recognition of Printed Hanguel Characters Using Neocognitron Approach S.Y. Bang, Pohang Institute of Science and Technology 16:40-17:00 The Recognition of Korean Characters by Neural Networks Chong-Ho Choi, Yun-Ho Jeon, and Myung-Chan Kim, Seoul National University 17:00-17:20 Character Recognition Using Iterative Autoassociation with the Tagged Classification Fields Sung-Il Chien, Kyungpook National University 17:20-17:40 Sejong-Net: A Neural Net Model for Dynamic Character Pattern Recognition Hyukjin Cho, Jun-Ho Kim, and Yillbyung Lee, Yonsei University 17:40-18:00 Hangul Recognition Using Neocognitron with Selective Attention Eun Jin Kim and Yillbyung Lee, Yonsei University ------------------------------ Subject: Re: Intel N64 From: steinbac@hpl-opus.HP.COM (Gunter Steinbach) Organization: HP Labs, High Speed Electronics Dept., Palo Alto, CA Date: 12 Apr 90 23:18:28 +0000 I don't have info on the N64 handy, but I just clipped an article from Electronic Engineering Times (Apr.9,90,p.2) with an even wilder claim. It is short enough to type in - without permission, of course: Intel, Nestor plan fastest neural chip Providence, R.I. - Nestor Inc. and Intel Corp (Santa Clara) have landed a $1.2 million contract from Darpa to fabricate the world's fastest neural network microchip. The target speed for the N1000 is 150 billion interconnections per second. The N1000, to be fabricated in Intel's EEPROM memory operation, wil have over 1000 neurons, using 250000 EEPROM cells for its synaptic weights and bias signals. It will be a single, standalone chip custom-tailored to realize Nestor's patented neural model, called restricted-coulomb energy (RCE). A special version of its development system will control a state machine that allows the chip to learn by programming its EEPROM. [[ For us Europeans, that's 150*10^9 interconnections/s. ]] Guenter Steinbach gunter_steinbach@hplabs.hp.com ------------------------------ Subject: Re: Intel N64 From: samsung!sdd.hp.com!elroy.jpl.nasa.gov!aero!aerospace.aero.org!plonski@think.com (Mike Plonski) Organization: The Aerospace Corporation Date: 16 Apr 90 23:22:50 +0000 Intel released the details for a neural network chip at the 1989 IJCNN. You may want to look at the following paper. "An Electrically Trainable Artifical Neural Network with 10240 "Floating Gate" Synapses. By Mark Holler, et. al., IJCNN, June 18-22 1989, Vol II,` pages 191-196. --------------------------------------------------------------------------- . . .__. The opinions expressed herein are soley |\./| !__! Michael Plonski those of the author and do not represent | | | "plonski@aero.org" those of The Aerospace Corporation. _______________________________________________________________________________ ------------------------------ Subject: Connections among nodes within a layer From: eepgszl@EE.Surrey.Ac.UK (LI S Z) Organization: University of Surrey, Guildford, Surrey, UK. GU2 5XH Date: 15 Apr 90 12:39:06 +0000 It seems to me that multilayer NN is generally modeled like this: Layer 3 O O ^\ /^ | \/ | | /\ | |/ \| Layer 2 O O ^\ /^ | \/ | | /\ | |/ \| Layer 1 O O There are connections among nodes between layers, but no connections among nodes WITHIN a layer. Why? For simplicity or neurologically it is so? If we connect nodes within each layer, it becomes like this: Layer 3 O<-->O ^\ /^ | \/ | | /\ | |/ \| Layer 2 O<-->O ^\ /^ | \/ | | /\ | |/ \| Layer 1 O<-->O The model includes the former one, and should be more powerful. Why not? Can anyone explain or tutor a bit, from computational viewpoints and/or from neuro-bio-physio-anatomo-logy viewpoints in particular. Thanks a lot. Stan ------------------------------ Subject: Re: Connections among nodes within a layer From: usenet@nlm-mcs.arpa (usenet news poster) Organization: National Library of Medicine, Bethesda, Md. Date: 15 Apr 90 19:14:48 +0000 >There are connections among nodes between layers, >but no connections among nodes WITHIN a layer. Why? One reason for excluding intra-layer connections is that you lose deterministic behavior of the net. A flip-flop can be created as a simple net with two nodes on the same layer each inhibiting the other. The system has two stable states (1,0) and (0,1) which are equally good. In more complex nets you could end up with race conditions where results depended on the order of evaluation etc. Depending on you application such non-deterministic may not be all bad. It is, for example, a way of building memory into a net after the training is complete. David States ------------------------------ Subject: Re: Connections among nodes within a layer From: demers@odin.ucsd.edu (David E Demers) Organization: University of California, San Diego Date: 16 Apr 90 17:19:29 +0000 [... more deleted] If you look into the literature, you will find just about every topology possible. Competitive learning models generally have mutually inhibitory connections along a layer, so that the "winner" eventually drives the others down. See Kohonen, for example. Also Grossberg. Connections within layers has been used in semi-recurrent nets, for example, Mike Jordan's nets. The question of architecture is highly problem dependent. For pattern associations, there does not seem to be much advantage to connections within layers (though I haven't done a solid search of research results). Essentially, for feedforward nets, the task intended is to develop a mapping between the input vectors and their corresponding patterns. A feedforward network with one hidden layer can approximate any mapping (Cybenko; Hornik, Stinchecombe & White; others). The number of units, connections and training time for learning the mapping are known to only very loose bounds, but much work is being done in the area. Expect to see a few papers at ICNN in San Diego in June. Dave ------------------------------ Subject: Re: Connections among nodes within a layer From: orc!bu.edu!bucasb!slehar@decwrl.dec.com (Lehar) Organization: Boston University Center for Adaptive Systems Date: 17 Apr 90 13:57:28 +0000 Interconnections between neurons within a layer fall into the same category as recurrent networks of the form... Layer 2 ---O O--- | ^\ /^ | | | \/ | | | | /\ | | | |/ \| | Layer 1 -->O O<-- Such topologies are unpopular among many network modellers because they introduce a lot of complexity into the system. Specifically, the state of the system depends on it's own past state as well as on the inputs. That means that you cannot compute the values of the nodes in one iteration, but must take many little time steps and compute the whole network at each step. Nodal activations will build up and decay much like voltages on capacitors and inductors. The accuracy of the simulation depends critically on the size of the time steps, and is best done using some differential equation solving algorithm such as Runge Cutta, as is employed often for simulations of analog electronics circuits. Recurrent networks however are very common in the brain, and just about every neural pathway has a complementary pathway going in the other direction and just about all neural layers have lateral connections. For example the optic nerve from the retina to the lateral geniculate body is uni-directional, but from the geniculate to the visual cortex (v1) there is a bi-directional path, with as much information going towards your eye as there is going towards your brain. Also, the cells in the retina, the geniculate and the cortex are all richly interconnected among themselves. Certain neural modellers (most notably Grossberg) make use of lateral and recurrent pathways using dynamic system simulations. The principal advantage of such simulations is that complex and subtle behavior can be elicited from very simple and elegant architectures. The power of dynamic and recurrent architectures will only be fully realized when we liberate ourselves from digital simulation and can build directly in analog hardware. In the meantime, such systems are still the best way to model the brain directly where your priorities are not speed and efficiency, but rather modelling accuracy of the biological system. (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O) (O)((O))((( slehar@bucasb.bu.edu )))((O))(O) (O)((O))((( Steve Lehar Boston University Boston MA )))((O))(O) (O)((O))((( (617) 424-7035 (H) (617) 353-6425 (W) )))((O))(O) (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O) ------------------------------ Subject: Re: Connections among nodes within a layer From: elroy.jpl.nasa.gov!aero!robert (Bob Statsinger) Date: 17 Apr 90 19:52:06 +0000 >There are connections among nodes between layers, >but no connections among nodes WITHIN a layer. Why? Look at Grossberg's Adaptive Resonance Theory. It DOES use connections within a layer to implement competitive learning. >For simplicity or neurologically it is so? Simplicity and computational tractability. Bob ------------------------------ Subject: Is there any quantitative measure of accuracy of a bp network? From: joshi@wuche2.wustl.edu (Amol Joshi) Organization: Washington University, St. Louis MO Date: 15 Apr 90 21:49:21 +0000 hi folks! i have to clarify my thoughts about how to interpret outputs obtained from a back-propagation neural network and also learn what is the practice. so, if you neural network gurus let me know your opinions about what follows, i would greatly appreciate it. this is a typical problem, i am sure most of you have encountered it at least once: say a back prop nn is to be used as a pattern classifier. i train a net so that my network gives values close to 0.995 to the desired nodes. (let us assume that the patterns are discrete - i.e. not more than one output node is required to flag any pattern in the domain). now, i use this trained network on noisy data and obtain values which are not `perfect'. let's say that in case I, the network flags the node indicative of a particular pattern with an output value of 0.45. the rest of the nodes have values which are *much lesser* than 0.45 (how much is *much lesser* is vague, but say any value of the order of 2 magnitudes lesser is *much lesser* - i.e. anything less than 0.0045). in my interpretation i call this performance as 100% accurate. however, i say that the network is only 45% certain of its own performance. what i am saying is that as long as the output node value is dominating other values in the output layer, my network is performing well if not perfectly. on the other hand, say in case II, if my network assigns values of 0.90 and 0.85 to two nodes (where only one of them is supposed to be flagged with a high value), even though the values are close to 1.0, i don't consider my network producing `accurate' results (but at the same time i have no way of quantifying the accuracy of this network). what is the common practice in interpreting the output values? is there any quantitative measure for accuracy of the network? could you please let me know your opinions? thanx. :amol ----------------------------------------------------------- Amol Joshi | joshi@wuche2.wustl.edu Department of Chemical Engineering | Washington University in St. Louis.| ------------------------------ Subject: Back Propagation for Training Every Input Pattern with Multiple Output From: cs.utexas.edu!uwm.edu!mrsvr.UUCP!neuron.uucp!ravula@tut.cis.ohio-state.edu (Ramesh Ravula) Date: 17 Apr 90 17:13:35 +0000 Has anyone used training the back propagation algorithm where every input pattern has to be associated with multiple output patterns. If so, what version (conventional, recurrent, etc.) of the back prop algorithm did you use?? I would be interested in knowing the results especially the learning times etc. I would also appreciate if any one could point any publications in this area. Please reply to my e-mail address given below. Ramesh Ravula GE Medical Systems Mail W-826 3200 N. Grandview Blvd. Waukesha, WI 53188. email: {att|mailrus|uunet|phillabs}!steinmetz!gemed!ravula or {att|uwvax|mailrus}!uwmcsd1!mrsvr!gemed!ravula ------------------------------ Subject: Re: Back Propagation for Training Every Input Pattern with Multiple Output From: thomasp@lan.informatik.tu-muenchen.dbp.de (Patrick Thomas) Organization: Inst. fuer Informatik, TU Muenchen, W. Germany Date: 20 Apr 90 16:43:45 +0000 I don't have it at hand, but I remember a net architecture by Jordan described in the 3rd PDP book, "Explorations in Parallel Distributed Processing", McClelland/Rumelhard, MIT Press, which would fit your needs. It's called something with SEQUENTIAL and backprop-trained a net to associate a sequence of output patterns (actually four of them, I think) with an input pattern. This gave you a kind of "command pattern" as input which initiated a sequence of "action patterns" as output. The trick was to divide the input layer and to have feedback connections from the output layer to one part of the so-called input layer. This part with the feedback connections and NO external input finally learned a representation of the output sequence. This was by far the most interesting model simulated with the PDP software but it's a long time ago, and I don't remember the details. The reference in the PDP book should be around "Jordan (1986)" or so. Patrick ------------------------------ Subject: servo systems From: voder!dtg.nsc.com!andrew@ucbvax.Berkeley.EDU (Lord Snooty @ The Giant Poisoned Electric Head ) Organization: National Semiconductor, Santa Clara Date: 17 Apr 90 23:32:21 +0000 I am interested in compiling a few references on the application of NNs to motion control systems - robotics and the like. If there's sufficient interest, I'll post the results. thanks in advance, andrew ........................................................................... Andrew Palfreyman andrew@dtg.nsc.com Albania during April! ------------------------------ Subject: Re: servo systems From: plonski@aerospace.aero.org (Mike Plonski) Organization: The Aerospace Corporation Date: 19 Apr 90 18:58:26 +0000 The latest IEEE Control Systems Magaine is a special issue on Neural Nets. %\def\Zzz{Special Issue on Neural Networks} %J |IEECSM| %A \Zzz %V 10 %N 3 %D |APR| 1990 %K Special Issue on Neural Networks in Control Systems %X {\bf Table of Contents:} Neural Networks in Control Systems; Associative Memories via Artificial Neural Networks; Neural Networks for Self-Learning Control Systems; Modeling Chemical Process Systems via Neural Computations; Neural Networks for System Identification; A Comparison Between CMAC Neural Nettwork Control and Two Traditional Adaptive Control Systems; Back-Propagation Neural Networks for Nonlinear Self-Tuning Adaptive Control; Use of Neural Networks for Sensor Failure Detection in a Control System; Learning Algorithms for Perceptrons Using Back-Propagation with Selective Updates; Neuromorphic Pitch Attitude Regulation of an Underwater Telerobot; Mobil Robot Control by a Structured Hierarchical Neural Network; Integrating Neural Networks and Knowledge-Based Systems for Intelligent Robotic Control; ------------------------------------------------------------------------------ . . .__. The opinions expressed herein are soley |\./| !__! Michael Plonski those of the author and do not represent | | | "plonski@aero.org" those of The Aerospace Corporation. _______________________________________________________________________________ ------------------------------ Subject: feature extraction in pictures (In English) From: andrew@karc.crl.go.jp (Andrew Jennings) Organization: Communications Research Laboratory Date: 15 May 90 02:56:38 +0000 The problem: Given a set of pictures (possibly quite large) I want to break up each picture into a set of objects, later to be used for retrieval. So if for example a picture features a baseball cap, this is stored as a feature that can be used for later retrieval. This seems to me to be a good area to apply self-organising networks. I am interested in pointers to the literature, and contacting others who are interested in this problem. Thanks. Andrew Jennings Kansei Advanced Research Center, 1990: year of Communications Research Laboratory, the demons andrew@crl.go.jp Kobe, Japan ------------------------------ Subject: Back-propagation/NN benchmarks From: voder!nsc!taux01!cyusta@ucbvax.Berkeley.EDU ( Yuval Shachar) Organization: National Semiconductor (IC) Ltd, Israel Date: 15 May 90 08:23:19 +0000 The following issues have been on my mind for a while now, so I thought I may as well lighten the burden a little. It seems to me that about 80% of the population of the galaxy are , at least in their spare time, trying to improve the back-propagation algorithm, be it propagation, percolation or imitation :-). It also seems that most of the methods succeed to some extent, and that is where I'm a little confused: 1. There are no strict performance criteria for bp networks Many papers quote the number of cycles. This is meaningless when comparing with conjugate gradient techniques for example, since there the number of cycles is greatly reduced, but each cycle involves line minimizations that require many evaluations of the error function (i.e feed-forward passes). Also, very small networks (e.g the 2-2-1 classic for the XOR problem) seem to be missing the point a little. The same goes for random starting points in error-space, unless the points location in error-space is well understood. 2. There are no standard benchmarks for (bp) networks This is very similar to the first point. A benchmark should consider a moderately sized network (i.e input space and out space dimensions), and even more important, a moderately sized problem which is WELL UNDERSTOOD, i.e the error landscape is known, has some local minima (sigh), narrow valleys and all the other features we have learned to love. (I even recall reading a paper by R. Hecht-Nielsen mentioning that they ran a complete scan of the error-space of a NN for more that a week on a Cray, before they had the full picture). The benchmark should include several starting points in error-space and a well defined stopping criterion. It should also have a large set of vectors to test the network after convergence ,both for learning precision and for extrapolation (generalization) ability. I would even go further and expect some sort of a standard specification format for neural network input, structure and weight state etc, so that anyone writing a simulator would be able to use these benchmarks with no difficulty. It seems to me that Minsky and Paperts Perceptrons is still one of the most important books written in the field, and its conclusions are still ignored by many researches. (I know I sin by oversimplifying and generalizing things a little, but, hey, I'm a neural network myself :-)) Finally, I am currently at a stage where such a benchmark would help me check my own contribution to bp improvements (yes, I'm also a part of these 80% ..). If anybody out there can contribute any kind of a nn benchmark, or provide a pointer to one, I will appreciate it. Otherwise I guess I will spend some time on doing just that, but will be glad to share it with whoever is interested. Any inputs are welcome. ------------------------------ Subject: Re: Back-propagation/NN benchmarks From: ins_atge@jhunix.HCF.JHU.EDU (Thomas G Edwards) Organization: The Johns Hopkins University - HCF Date: 15 May 90 20:46:08 +0000 Well, there are some "standard" benchmarks. XOR...to make sure the network method has some hope of working (similar to hooking an Op-Amp up as a follower before you begin testing it to make sure it has some hope of working) Binary Encoders...A binary number of n digits is applied as input. The network has m hidden units s.t. n>m, often m=log2(n), and the output is trained to replicate the input pattern with n output units. Chaotic Time Series...Used by Lapedes and Farber (for BP I think), and Moody and Darken (for Locally Receptive Fields). The network is trained to predict future values of the series based on a few values from the series in the past. Learning to Oscillate...for recurrent networks, train the network to be a sine or squarewave generator. Two Spirals Problem...The network is trained to tell whether a given set of x,y coordinates lies on spiral #1 or spiral #2. The two spirals are intertwined. Very difficult for backpropagation. Doable in quickprop, even reasonable with cascade-correlation. -Thomas Edwards ------------------------------ Subject: Re: Back-propagation/NN benchmarks From: golds@fjcnet.GOV (Rich Goldschmidt) Organization: Federal Judicial Center, Washington, D.C. Date: 16 May 90 12:54:17 +0000 One of the standard benchmarks for neural networks is the concentric sprials problem, originally proposed by Alexis Weiland. It is described in the Byte article by Touretzkey and (?). The problem is to classify which of the two adjacent spirals you are on. There are some nice examples of the representations that hidden units take on in learing to solve this problem in that Byte article. Rich Goldschmidt uunet!fjcp60!golds or golds@fjcp60.uu.net ------------------------------ Subject: Re: Back-propagation/NN benchmarks From: cyusta@taux01.UUCP ( Yuval Shachar) Organization: National Semiconductor (IC) Ltd, Israel Date: 17 May 90 06:49:30 +0000 >Well, there are some "standard" benchmarks. > > ... > >XOR ... >Binary Encoders ... >Chaotic Time Series ... Yes, these are all good examples of classic NN problems. It takes a lot more to make benchmarks out of them however. If in addition we would have a set of initial weight-states and a matching number for each one specifying the performance of the net for that initial state, and if these starting points were interesting enough, and if the performance criteria were well defined etc we would have something like a benchmark. The fact is that many researchers go through analyzing these problems before they can use them on their own network/algorithm. Apart from the fact that often this extra work could be spared, it would also grant more meaning to posted results. Yuval Shachar cyusta@taux01.nsc.com cyusta@nsc.nsc.com shachar@taurus.bitnet shachar@math.tau.ac.il National Semiconductor (Israel) P.O.B. 3007, Herzlia 46104, Israel Tel. +972 52 522310 TWX: 33691, fax: +972-52-558322 ------------------------------ End of Neuron Digest [Volume 6 Issue 35] ****************************************