
IEEE TFS: Abstracts of Published Papers, vol. 2, no. 3
A fuzzy neural network and its application to pattern recognition
Defines four types of fuzzy neurons and proposes the structure of a fourlayer feedforward fuzzy neural network (FNN) and its associated learning algorithm. The proposed fourlayer FNN performs well when used to recognize shifted and distorted training patterns. When an input pattern is provided, the network first fuzzifies this pattern and then computes the similarities of this pattern to all of the learned patterns. The network then reaches a conclusion by selecting the learned pattern with the highest similarity and gives a nonfuzzy output. The 26 English alphabets and the 10 Arabic numerals, each represented by 16*16 pixels, were used as original training patterns. In the simulation experiments, the original 36 exemplar patterns were shifted in eight directions by 1 pixel (6.25% to 8.84%) and 2 pixels (12.5% to 17.68%). After the FNN has been trained by the 36 exemplar patterns, the FNN can recall all of the learned patterns with 100% recognition rate. It can also recognize patterns shifted by 1 pixel in eight directions with 100% recognition rate and patterns shifted by 2 pixels in eight directions with an average recognition rate of 92.01%. After the FNN has been trained by the 36 exemplar patterns and 72 shifted patterns, it can recognize patterns shifted by 1 pixel with 100% recognition rate and patterns shifted by 2 pixels with an average recognition rate of 98.61%. The authors have also tested the FNN with 10 kinds of distorted patterns for each of the 36 exemplars. The FNN can recognize all of the distorted patterns with 100% recognition rate. The proposed FNN can also be adapted for applications in some other pattern recognition problems.
A fuzzy controller improving a linear model following controller for
motor drives
Since the dynamic response trajectory of a traditional fuzzy controller can not be quantitatively controlled, a fuzzy model following controller is proposed in this paper. In the proposed controller, an output feedback linear model following controller (LMFC) is first designed according to the roughly estimated plant model to let its response follow the output generated by a reference model. Then a model following error driven control signal is synthesized such that good model following characteristics can be preserved at various operating conditions. The proposed controller is applied to the speed control of an induction motor drive. Dynamic signal analysis of the model following behavior is made and the procedure for constructing the control algorithms is described in detail. The performance of the drive and the effectiveness of the proposed controller are demonstrated by some simulated and experimental results.
Fuzzy membership function based neural networks with applications to the
visual servoing of robot manipulators
It is shown that there exists a nonlinear mapping which transforms image features and their changes to the desired camera motion without measuring of the relative distance between the camera and the object. This nonlinear mapping can eliminate several difficulties occurring in computing the inverse of the feature Jacobian as in the usual featurebased visual feedback control methods. Instead of analytically deriving the closed form of this mapping, a fuzzy membership function (FMF) based neural network incorporating a fuzzyneural interpolating network is proposed to approximate the nonlinear mapping, where the structure of the FMF network is similar to that of radial basis function neural network which is known to be very effective in the function approximation. Several FMF networks are trained to be capable of tracking a moving object in the whole workspace along the line of sight. For an effective implementation of the proposed FMF network, an image feature selection process is investigated, and the required fuzzy membership functions are designed. Finally, several numerical examples are presented to show the validity of the proposed visual servoing method.
Fuzzy model based control: stability, robustness, and performance issues
A nonlinear controller based on a fuzzy model of MIMO dynamical systems is described and analyzed. The fuzzy model is based on a set of ARX models that are combined using a fuzzy inference mechanism. The controller is a discretetime nonlinear decoupler, which is analyzed both for the adaptive and the fixed parameter cases. A detailed stability analysis is carried out, and the main result is that the closed loop is globally stable and robust with respect to unstructured uncertainty, which may include modeling error and disturbances. In addition, bounds on the asymptotic and transient performance are given. The main assumptions on the system and model are that they must not have strong nonminimumphase effects, except timedelay, and the unstructured uncertainty must not be too large. A simulation example illustrates some of the properties of the modeling method and model based control structure.
Fuzzy controller design by using neural network techniques
This paper investigates the relationship between the piecewise linear fuzzy controller (PLFC), in which the membership functions for fuzzy variables and the associated inference rules are all in piecewise linear forms, and a Gaussian potential function network based controller (GPFNC), in which the network output is a weighted summation of hidden responses from a series of Gaussian potential function units (GPFU's). Systematic procedures are proposed for transformation from a PLFC to its GPFNC counterpart, and vice versa. Based on these transformation principles, a series of systematic and feasible steps is presented for the design of an optimized PLFC (PLFC*) by using neural network techniques. In the design procedures, the simplified PLFC is used as the initial controller structure, then a GPFNC, which gives the approximate control response to the initially given PLFC, is found for further optimization. A neutralization process is used to demonstrate the feasibility and the potential applicability of these intelligent controllers on the regulation of highly nonlinear chemical processes. These abstracts are posted in order to accelerate dissemination of evolving Fuzzy Systems information. The abstracts are from papers published in the IEEE Transactions on Fuzzy Systems (TFS). 


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