Fuzzy time series approaches, which do not require the strict assumptions of traditional time series approaches, generally consist of three stages. These stages are called as the fuzzification of crisp time series observations, the identification of fuzzy relationships and the defuzzification, respectively. All of these stages play an important role on the forecasting performance of the model. ...

Computing the output of fuzzy systems usually passes through three stages i.e. fuzzification, inference, and defuzzification. The computation of the output requires too many operations and considerable time. This apparently discourages the use of fuzzy systems in time-critical applications. In this paper, a new two-stage representation of fuzzy systems, which reduces the operations needed to co...

This paper proposes a rule base compression method for fuzzy systems. The method is based on filtration of rules with identical linguistic values for the output that are known as non-monotonic rules. The filtration removes the redundant computations in the fuzzy inference with respect to the crisp values of the inputs to the fuzzy system. The method identifies the redundant rules after fuzzific...

This paper presents a systematic approach to designing dynamic metaheuristic fuzzy logic controller (FLC) control piece of non-linear plant. The developed is multiple-input–multiple-output (MIMO) system. However, with the proposed mechanism possible adapt it single-input–single-output (SISO) systems as well. During real-time operation, behavior influenced by particle swarm optimization (PSO) me...

Abstract: This paper presents the design methods for the designing and simulation of implication function (mamdani) using two stage CMOS operational amplifier in SPICE. This paper also presents how the first three steps of fuzzy system i.e. fuzzification, rule composition and implication process are merged and get the output for each rule. I have also calculated the electrical parameters like s...

The most important part of a Case-Based Reasoning system is the retrieval stage, where the system must find in a sometimes-huge case base, the best matching case or cases from which to produce the prediction for the outcome of a given situation. In this paper we propose a fuzzy logic based approach for identifying cases for the similarity measuring stage of case based reasoning systems. We comb...

The present paper is devoted to the transition from crisp domains of probability to fuzzy domains of probability. First, we start with a simple transportation problem and present its solution. The solution has a probabilistic interpretation and it illustrates the transition from classical random variables to fuzzy random variables in the sense of Gudder and Bugajski. Second, we analyse the proc...

Osteoporosis is a disease in which bones become fragile and more likely to break. can progress painlessly until it causes bone fracture or Dual Energy X-ray Absorptiometry (DEXA) costly not accessible easily so we are using Fuzzy Inference system predict osteoporosis. In this fuzzy logic, collect risk factors rules for osteoporosis build interface take inputs predicts if person has the followin...

One of the most promising areas scientific research in field analysis, forecasting and modelling economic phenomena processes is fuzzy logic. In article with use logic apparatus (Fuzzy Logic toolkit MATLAB software package) a model for assessing development manufacturing indicators developed. Key evaluating activities energy enterprises are analysed. The based on microeconomic enterprises’ effi...