The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

ANFIS: adaptive-network-based fuzzy inference system

The organization of behavior: A neuropsychological theory

The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

Fuzzy logic, neural networks, and soft computing

Prof. Zadeh presented a comprehensive lecture on fuzzy logic, neural networks, and soft computing. In addition, he lead a spirited discussion of how these relatively new techniques may be applied to safety evaluation of time variant and nonlinear structures based on identification approaches. The abstract of his lecture is given as follows.

/pdf/fuzzy-logic-neural-networks-and-soft-computing-363h6xpbu4.pdf

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. >

Multilayer perceptron, fuzzy sets, and classification

NN-driven fuzzy reasoning

Possibilistic linear regression analysis for fuzzy data

Introduction to Fingerprint Recognition, U. Halici, L.C. Jain, and A. Erol Fingerprint Feature Processing Techniques and Poroscopy, A.R. Roddy and J.D. Stosz Fingerprint Sub-Classification: A Neural Network Approach, G.A. Drets and H.G. Leljecstroem A Gabor Filter-Based Method for Fingerprint Identification, Y. Hamamoto Minutiae Extraction and Filtering from Gray-Scale Images, D. Maio and D. Maltoni Feature Selective Filtering for Ridge Extraction, A. Erol, U. Halici, and G. Ongun Introduction to Face Recognition, A.J. Howell Neural Networks for Face Recognition, A.S. Pandya and R.R. Szabo Face Unit Radial Basis Function Networks, A.J. Howell Face Recognition from Correspondence Maps, R.P. Wurtz Face Recognition by Elastic Bunch Graph Matching, L. Wiskott, J.-M. Fellous, N. Kruger, and C. von der Malsburg Facial Expression Synthesis Using Radial Basis Function Networks, I. King and X.Q. Li Recognition of Facial Expressions and Its Application to Human Computer Interaction, T. Onisawa and S. Kitazake

Intelligent biometric techniques in fingerprint and face recognition

The authors propose a learning method for fuzzy inference rules by a descent method. From input-output data gathered from specialists, the inference rules expressing the input-output relation of the data are obtained automatically. The membership functions in the antecedent part and the real number in the consequent part of the inference rules are tuned by means of the descent method. The learning speed and the generalization capability of this method are higher than those of a conventional backpropagation type neural network. This method has the capability to express the knowledge acquired from input-output data in the form of fuzzy inference rules. Some numerical examples are described to show these advantages over the conventional neural network. An application of the method to a mobile robot that avoids a moving obstacle and its computer simulation are reported. >

A learning method of fuzzy inference rules by descent method

In order to identify free amino acids (FAA) that are importantas intracellular osmolytes in Crassostrea gigas, we investigatedthe change in FAA content in the mantle exposed to an abrupt decreaseor increase in salinity. In hypo-osmotic adaptation, most FAA showedremarkable and synchronous decreases from 2 to 8 h, suggestingthat the non-selective efflux of FAA was mainly responsible forthe decrease in FAA. Taurine that accounted for approximately 80% oftotal FAA content contributed most significantly to the hypo-osmoticadaptation. In hyper-osmotic adaptation, significant increases inglycine, alanine, β-alanine, proline, arginine and taurinewere observed. Of these, alanine showed an immediate increase thatis important to short-term adaptation to hyper-osmolality, whiletaurine showed a slower and substantial increase that contributesto a long-term adaptation to hyper-osmolality.

/pdf/effect-of-salinity-change-on-free-amino-acid-content-in-2nsd2qdi2c.pdf

Isao Hayashi

Papers

NN-driven fuzzy reasoning

Possibilistic linear regression analysis for fuzzy data

Intelligent biometric techniques in fingerprint and face recognition

A learning method of fuzzy inference rules by descent method

Effect of salinity change on free amino acid content in Pacific oyster.