Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural NetworksPublished 1 January 1992
Sankar K. Pal, Sushmita Mitra
Citations1,110
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described, and the results are compared with those of the conventional MLP, the Bayes classifier, and other related models.
Abstract
A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and other related models.
Keywords
Computer Science
Springer series in information sciencesSelf-Organization and Associative Memory
8,770 Citations1989Teuvo Kohonen
IEEE ASSP MagazineAn introduction to computing with neural nets
7,858 Citations1987Richard P. Lippmann
European Journal of Operational ResearchFuzzy sets, uncertainty, and information
3,193 Citations1989
The fuzzy sets uncertainty and information is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
Information and ControlA new approach to clustering
1,564 Citations1969Enrique H. Ruspini
A new method of representation of the reduced data, based on the idea of “fuzzy sets,” is proposed to avoid some of the problems of current clustering procedures and to provide better insight into the structure of the original data.
Parallel Networks that Learn to Pronounce English Text
1,556 Citations1989Terrence J. Sejnowski
Artificial IntelligenceConnectionist learning procedures
1,447 Citations1989Geoffrey E. Hinton
These relatively simple, gradient-descent learning procedures work well for small tasks and the new challenge is to find ways of improving their convergence rate and their generalization abilities so that they can be applied to larger, more realistic tasks.
Neural NetworksAn introduction to neural computing
1,206 Citations1988Teuvo Kohonen
A brief survey of the motivations, fundamentals, and applications of artificial neural networks, as well as some detailed analytical expressions for their theory.
Neural NetworksCreating artificial neural networks that generalize
593 Citations1991Jocelyn Sietsma, R.J.F. Dow
A technique to test the hypothesis that multilayered, feed-forward networks with few units on the first hidden layer generalize better than networks with many units in the first layer finds the hypothesis to be false for networks trained with noisy inputs.
International Journal of Approximate ReasoningNN-driven fuzzy reasoning
581 Citations1991Hideyuki Takagi, Isao Hayashi
A new fuzzy reasoning that can solve two problems of conventional fuzzy reasoning by combining an artificial neural network (NN) and fuzzy reasoning is proposed, capable of automatic determination of inference rules and adjustment according to the time-variant reasoning environment because of the use of NN in fuzzy reasoning.
Machine LearningCONNECTIONIST LEARNING PROCEDURES11This chapter appeared in Volume 40 of Artificial Intelligence in 1989, reprinted with permission of North-Holland Publishing. It is a revised version of Technical Report CMU-CS-87-115, which has the same title and was prepared in June 1987 while the author was at Carnegie Mellon University. The research was supported by contract N00014-86-K-00167 from the Office of Naval Research and by grant IST-8520359 from the National Science Foundation.
523 Citations1990Geoffrey E. Hinton
These relatively simple, gradient-descent learning procedures work well for small tasks, and the new challenge is to find ways of improving their convergence rate and their generalization abilities so that they can be applied to larger, more realistic tasks.
Medical Entomology and ZoologyFuzzy Mathematical Approach to Pattern Recognition
512 Citations1986Sankar K. Pal, Dwijesh K. Dutta-Majumder
Results of investigations, both experimental and theoretical, are presented into the effectiveness of fuzzy algorithms as classification tools in some problems concerned with the field of pattern recognition and image processing.
Improving the convergence of back-propagation learning with second-order methods
333 Citations1989Stephen Becker, Yann LeCun
It is suggested that HSP27 plays a new role in the antiapoptotic action triggered by 17β-estradiol by modulating caspase-3 activity and stabilizing ERβ in skeletal muscle cells.
IEEE Transactions on Acoustics Speech and Signal ProcessingExperiments on neural net recognition of spoken and written text
263 Citations1988Dc Burr
Results indicate that neural networks and nearest-neighbor classifiers perform at near the same level of accuracy and a novel handwriting encoder is described.
IEEE Transactions on Acoustics Speech and Signal ProcessingLearned classification of sonar targets using a massively parallel network
246 Citations1988Rebecca Gorman, Terrence J. Sejnowski
The performance of a three-layered network was better than trained human listeners and the network generalized better than a nearest-neighbor classifier.
IEEE Transactions on Pattern Analysis and Machine IntelligenceIncorporating Fuzzy Membership Functions into the Perceptron Algorithm
235 Citations1985James M. Keller, Douglas J. Hunt
It is shown that the fuzzy perceptron, like its crisp counterpart, converges in the separable case.
IEEE Transactions on Systems Man and CyberneticsFuzzy Sets and Decisionmaking Approaches in Vowel and Speaker Recognition
188 Citations1977
Two decision algorithmic methods using weighted-distance functions and property sets are developed and implemented with the optimum size of the training set on a large number of Telugu speech sounds with a recognition score of 82 percent for vowels and 97 percent for the speaker.
Lecture notes in computer scienceLearning translation invariant recognition in a massively parallel networks
173 Citations1987Geoffrey E. Hinton
This paper describes a recently developed procedure that can learn to perform a recognition task and uses canonical internal representations of the patterns to identify familiar patterns in novel positions.
ComputerConnectionist Architectures for Artificial Intelligence
146 Citations1987Fahlman, Hinton
The authors concentrate here on connectionism's potential as a practical technology for building intelligent systems, and also some of the unsolved problems facing this approach.
Neural Information Processing SystemsNeural Net and Traditional Classifiers
139 Citations1987William Y. C. Huang, Richard P. Lippmann
It is demonstrated that two-layer perceptron classifiers trained with back propagation can form both convex and disjoint decision regions.
Artificial IntelligenceA parallel network that learns to play backgammon
126 Citations1989G. Tesauro, Terrence J. Sejnowski
A class of connectionist networks is described that has learned to play backgammon at an intermediate-to-advanced level and is suggested how these techniques might be used in applications of network learning to general large-scale, difficult “real-world” problem domains.
Computer Speech & LanguageLearning sets of filters using back-propagation
115 Citations1987David C. Plaut, Geoffrey E. Hinton
Further research is described on back-propagation for layered networks of deterministic, neuron-like units and an example in which a network learns a set of filters that enable it to discriminate formant-like patterns in the presence of noise.
Information SciencesBackpropagation neural networks for fuzzy logic
71 Citations1992James M. Keller, Hossein Tahani
Feedforward neural networks are proposed as a means of controlling this generality as well as providing the parallel computation necessary for fuzzy logic and it is demonstrated that these networks can learn and extrapolate complex relationships between antecedents and consequents for rules containing single and conjunctive antecedent clauses.
Information SciencesLinguistic recognition system based on approximate reasoning
65 Citations1992Sankar K. Pal, Deba Prasad Mandal
A linguistic recognition system based on approximate reasoning has been described which is capable of handling various imprecise input patterns and of providing a natural decision, thus providing a low rate of misclassification as compared to the conventional two-state system.
Pattern Recognition LettersFuzzy measures in determining seed points in clustering
42 Citations1986Sankar K. Pal, Prabhat Pramanik
Two membership functions in R have been defined such that the fuzzy measures posses maximum values when the crossover points/central points of the membership functions correspond to the points around which the data has a tendency to cluster.
Neural NetworksImage processing and pattern recognition in ultrasonograms by backpropagation
36 Citations1990Ronald H. Silverman, Andrew S. Noetzel
Ulasonic images of human eyes in which choroidal tumors were present were applied as input to a neural network utilizing the backpropagation algorithm and were capable of localizing and classifying tumors and were tolerant of differences in size, conformation, and orientation.
NASA Technical Reports Server (NASA)Intelligent control based on fuzzy logic and neural network theory
17 Citations1990Chuen-Chien Lee, Lotfi A. Zadeh
The thesis describes a self-learning rule-based system which integrates a fuzzy logic based controller with a cognitive neural model stimulating learning behavior and memory association and learns from experience and modifies its rule base for better control strategy.
The Journal of the Acoustical Society of AmericaContext-modulated discrimination of similar vowels using second-order connectionist networks
11 Citations1989Raymond L. Watrous
NASA STI Repository (National Aeronautics and Space Administration)Intelligent control based on fuzzy logic and neural net theory
10 CitationsLee, Chuen-Chien
NASA Technical Reports Server (NASA)Sunspot prediction using neural networks
9 Citations1990James A. Villarreal, Paul Baffes
The efforts of the Artificial Intelligence Section of the Mission Planning and Analysis Division of the Johnson Space Center involving the prediction of sunspot activity using neural network technologies are described.