Fuzzy Logic Membership Functions Research Articles

Fault Diagnosis Based on the Fuzzy‐Recurrent Neural Network

ABSTRACTA fuzzy‐recurrent neural network (FRNN) has been constructed by adding some feedback connections to a feedforward fuzzy neural network (FNN). The FRNN expands the modeling ability of a FNN in order to deal with temporal problems. A basic concept of the FRNN is first to use process or expert knowledge, including appropriate fuzzy logic rules and membership functions, to construct an initial structure and to then use parameter‐learning algorithms to fine‐tune the membership functions and other parameters. Its recurrent property makes it suitable for dealing with temporal problems, such as on‐line fault diagnosis. In addition, it also provides human‐understandable meaning to the normal feedforward multilayer neural network, in which the internal units are always opaque to users. In a word, the trained FRNN has good interpreting ability and one‐step‐ahead predicting ability. To demonstrate the performance of the FRNN in diagnosis, a comparison is made with a conventional feedforward network. The efficiency of the FRNN is verified by the results.

Determination of fuzzy logic membership functions using genetic algorithms

Genetic algorithm (GA) is an adaptive search technique based on natural selection and genetic rules. GA takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the kind of membership functions. Proposed method has been implemented using a developed GA program for a single input and output fuzzy system.

A neuro-fuzzy controller for mobile robot navigation and multirobot convoying

A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for nonlinear dynamic control problems. NiF-T architecture comprises of three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) a Rule Neural Network (RNN), and (3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors-wall following, hall centering, and convoying, their RNN's are trained only for a few hundred iterations and so are their ORNN's trained for only less than one hundred iterations to learn their parent rule sets.

Subthreshold membership function circuit

A new subthreshold fuzzy logic membership function circuit is proposed which consumes only a small fraction of the current required by our previous design. This lower power consumption will enable the implementation of a modular architecture without incurring prohibitive power consumption penalties.

A Tractor Engine Control System Based on a Fuzzy Logic and a Genetic Algorithm

Abstract This paper describes the development of a fuzzy logic control system to control the speed of a diesel engine by adjusting the rack of the injection pump. A genetic algorithm technique was used to simultaneously design the fuzzy logic membership functions and rules. The engine tests were conducted; the results indicate that the response of the fuzzy control system design by a genetic algorithm had a faster response than the other controllers tested. The fuzzy control system also had a higher engine speed stability along with reduced fuel consumption.

A stochastic interpretation of membership functions

A stochastic interpretation of the so called membership functions of fuzzy logic is presented. The membership functions are used to define a probability space, and the values of them may be interpreted as probabilities. It is believed that the stochastic interpretation will facilitate the analysis and understanding of fuzzy control.

Fuzzy rule-based networks for control

The authors present a method for learning fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: first, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an information-theoretic approach for induction of rules from discrete-valued data; and, finally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backer-upper control system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Analog CMOS realization of fuzzy logic membership functions

The design of a monolithic CMOS analog function synthesizer based on a current-mode algorithm and its application in fuzzy membership function synthesis is presented. The proposed circuits require only one reference current, independently of the course or the number of implemented functions. The networks are temperature and technology insensitive. Other features are a small chip area and a simple design process for any arbitrary functions. Matching considerations allow a prediction of the available approximation accuracy. Theoretical evaluations are validated by measurements of several membership functions fabricated in an experimental 1.0- mu m CMOS technology.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>