Membership Function Generator Research Articles

An overview of membership function generation techniques for pattern recognition

The estimation of membership functions from data is an important step in many applications of fuzzy theory. In this paper, we provide a general overview of several methods for generating membership functions for fuzzy pattern recognition applications. We discuss methods based on heuristics, probability to possibility transformations, histograms, nearest neighbor techniques, feed-forward neural networks, clustering, and mixture decomposition. We also illustrate these membership generation methods using synthetic and real data sets, and discuss the suitability and applicability of these membership function generation techniques to particular situations.

Hardware implementation of a membership function generator for fuzzy reasoning

The paper presents a simple analog circuit which can realize the membership functions necessary for a hardware implementation of fuzzy reasoning. The circuit, which is based on bipolar technology, arranges these transistors in such a way so as to produce a current spike similar to that observed in the CMOS inverter configuration during switching. The spike characteristic is then amplified to produce, as an output, either a current or a voltage. Additional inputs have also been included to produce the S, Z, λ, and Π outputs. The circuit can also take either a current or voltage as an input, thus adding flexibility to its operation. Simulation results which demonstrate the operation of the circuit using T-spice are presented; experimental results which support these are also shown.

An alternative approach for generation of membership functions and fuzzy rules based on radial and cubic basis function networks

The theoretically attractive fact that the radial basis function networks can be interpreted as fuzzy systems is of small importance for practical applications such as diagnosis and quality control with large numbers of inputs or hidden neurons, due to the lack of transparency of the resulting fuzzy systems. A novel method for the generation of fuzzy classification systems based on radial basis function networks with restricted Coulomb energy learning is presented. The neural network and the learning algorithm are modified for easy hardware implementation by introducing cubic basis functions. The proposed methods are tested with three application examples. The simulation results show the generation of compact, transparent fuzzy classification systems with good performance.

A novel analog fuzzy controller for intelligent sensors

A new analog fuzzy logic controller implemented in CMOS technology is described. The chosen membership function generator keeps the needed area for the inference engine very small while giving a big flexibility in the configuration of the membership function. The proposed solution for defuzzification gives an additional area reduction over earlier implementations. High speed, low power fuzzy controller hardware make the chip appropriate for intelligent sensor application. Simulation results as well as test measurements are presented and discussed to illustrate the properties and robustness of the proposed circuit.

Simulation of a Fuzzy-Logic Control System

We review fuzzy-control principles and show a simulation program permitting convenien t generation of membership functions and simple output interpolation. Interactive simulation is more than ordinarily important for economical fuzzy- controller design, which must reduce the number of membership classes and input/ output rules as much as possible. We use a personal computer and interactive editing to compare linear and fuzzy servo controllers. With 50 per cent class-membership overlap ("pseudo dither"), our fuzzy logic produces linear interpolation, and thus completely unimpaired servo performance even if we use only 2 fuzzy sets ("positive" and negative") each for error and output rate.

任意のファジィ評価関数によるファジィクラスタリング手法 : ファジィルール、メンバーシップ関数の自動生成法

任意のファジィ評価関数によるファジィクラスタリング手法 : ファジィルール、メンバーシップ関数の自動生成法