Asymmetric Membership Function Research Articles

Robust self-organizing fuzzy-neural control using asymmetric Gaussian membership functions

A robust self-organizing fuzzy-neural control (RSOFNC) system is proposed in this paper. The RSOFNC system is comprised of a self-structuring fuzzy neural network (SFNN) controller and a robust controller. The SFNN controller is the principal controller and the robust controller is designed to achieve L2 tracking performance. In the SFNN controller design, a SFNN with the asymmetric Gaussian membership functions is used to online approximate an ideal controller via the structure and parameter learning phases. The structure learning phase consists of the growing of membership functions and the pruning of fuzzy rules, and thus the SFNN can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. Finally, the proposed RSOFNC system is applied to control a second-order chaotic system. The simulation results show that the proposed RSOFNC system can achieve favorable tracking performance.

Design of a Fuzzified Terminal Guidance Law

A new fuzzified terminal guidance law is proposed which employs the versatile feature of the fuzzy logic system to shape a beneficial trajectory for target interception. In the proposed scheme, the guidance law is partitioned into three stages in accordance with the relative range between the interceptor and target. Estimated line-of-sight angle, line-of-sight angle rate, line-of-sight angular acceleration and relative range are used to constitute the rule antecedent of the guidance law to shape an appropriate flight trajectory for engagement. Asymmetric membership functions and guidance rules are developed for satisfactory engagement performance and robustness. Engagement performance is well proven by extensive computer simulations.

Comparison of Fuzzy Logic Based DC Voltage Control Strategies for a Three Phase Parallel Active Filter

Abstract This paper is on the performance compazison of vazious PI type Fuzzy Logic Controllers for a three phase, three-wired Pazallel Active Filter (PAF) system, consisting of an Insulated Gate Bipolar Transistor (IGBT) based current controlled PWM Voltage Source Inverter. As a novelty in PAF applications, a Self Tuning Fuzzy Logic Controller is developed in order to improve the dynamic behavior of the system. The performance of the self tuning fuzzy control is compared with that of two Mamdani type fuzzy PI controllers with symmetric and asymmetric triangulaz membership functions, as well a classical linear PI controller. Simulations aze performed to evaluate the system performance in terms of maximum overshoot (Tm), rise time (tr), and settling time (ts) of the do capacitor voltage to the 2% of the reference do voltage. The results show that the self tuning fuzzy control improves the dynamic behavior of the PAF, while maintaining the improved Total Harmonic Distortion in the ac source current (THDias)...

Asymmetric Subsethood-Product Fuzzy Neural Inference System (ASuPFuNIS)

This paper presents an asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) that directly extends the SuPFuNIS model by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood-product network admits both numeric as well as linguistic inputs. Input nodes, which act as tunable feature fuzzifiers, fuzzify numeric inputs with asymmetric Gaussian fuzzy sets; and linguistic inputs are presented as is. The antecedent and consequent labels of standard fuzzy if-then rules are represented as asymmetric Gaussian fuzzy connection weights of the network. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. Despite the increase in the number of free parameters, the proposed model performs better than SuPFuNIS, on various benchmarking problems, both in terms of the performance accuracy and architectural economy and compares excellently with other various existing models with a performance better than most of them.

An asymmetry-similarity-measure-based neural fuzzy inference system

In this paper, a new asymmetry-similarity-measure-based neural fuzzy inference system (ASM-NFIS) is proposed. A pseudo-Gaussian membership function can provide a neural fuzzy inference system which has a higher flexibility and can approach the optimized result more accurately. An on-line self-constructing learning algorithm is proposed to automatically construct the ASM-NFIS. It consists of structure learning and parameter learning that would create adaptive fuzzy logic rules. The structure learning is based on the similarity measure of asymmetric Gaussian membership functions, and the parameter learning is based on a supervised gradient descent method. Computer simulations were conducted to illustrate the performance and applicability of the proposed model.

A New Fuzzy Inference Method for Symbolic Stability Analysis of Multigranular Intelligent Control System

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

Construction of differentiable membership functions

In many applications of fuzzy logic it is of special interest to have a transfer function with good properties regarding differentiability. To that end it is desirable to have continuously differentiable membership functions with only few parameters. In this paper we propose a class of symmetrical and asymmetrical membership functions of exponential order. Moreover, we present a class of more flexible membership functions by construction.

A data converter for analog/fuzzy‐logic interface

AbstractInput signals are often fed as analog quantities into fuzzy logic processing systems. This paper proposes a method for configuring interface necessitated by fuzzy logic systems featuring analog inputs and digital processing. This interface is a data converter which processes input analog voltage signals and outputs digital signals suitable for fuzzy logic processing.The conversion principle is based on the concept of level sets within the fuzzyset theory, i.e., depending on whether a given analog signal belongs to level sets, the signal is quantized and converted to membership grade, simultaneously. Having programmable conversion characteristics, it can realize typical membership functions such as Z‐, ‐, and S‐functions.The operation of the converter, which is configured of counters, a memory, a digital‐to‐analog converter, and a comparator, is verified experimentally as regards a non‐monotonic and asymmetric membership function. the conversion accuracy is then discussed and the causes for error are described.