The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on developing fuzzy time series (FTS) algorithm. MWTS has overcome certain limitations of FTS, such as repetition logic relationships and weight considerations relationships. main challenge the MWFTS absence standardized rules determining partition intervals. This study compares model to methods Genetic Algor...

NNPC has been used widely to control nonlinear systems. However traditional gradient decent algorithm (GDA) needs a large computational cost, so that NNPC is not acceptable for systems with rapid dynamics. To apply NNPC in fast control of mobile robots, the paper proposes an improved optimization technique, particle swarm optimization with controllable random exploration velocity (PSO-CREV), to...

Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The Fuzzy C-means algorithm (FCM) and the Possibilistic C-means algorithm (PCA) have been widely used. There is also the generalized possibilistic algorithm (GPCA). GPCA was proposed recently and is a general form of the previous algorithms. These clustering algorithms can be trapped to the local opt...

This chapter maps out the development of the PSO based Functional Link Interval Type-2 Fuzzy Neural System (FLIT2FNS) model used to forecast the stock market indices. In the process, it discusses the architecture of Functional Link Artificial Neural Network (FLANN), FLANN & Type-1Fuzzy Logic System (Type1FLS) and the differences between Type-1FLS and Interval Type-2 Fuzzy Logic System (IT2FLS)....

The idea of developing a multi-joint rehabilitation robot is to satisfy the demands for recovery lower limb functionality in hemiplegic impairments and assist physiotherapists with their therapy plans. This work aims at implement Lyapunov Adaptive Swarm-Fuzzy Logic Control (LASFC) strategy 4-degree freedom (4-DoF) Lower Limb Assistive Robot (LLAR) application, which control law an integration s...

In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs). Model updating techniques use the measured data to improve the accuracy of numerical models of structures. However, the measured data are contaminated with experimental noise and the models are inaccurate due to randomness in the parameters. This kind of aleatory uncertainty is irreduc...

Function approximation is an important type of supervised machine learning techniques, which aims to create a model for an unknown function to find a relationship between input and output data. The aim of the proposed approach is to develop and evaluate a function approximation models using Radial Basis Function Neural Networks (RBFN) and Particles Swarm Optimization (PSO) algorithm. We propose...

Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the pa...

Abstract This paper considers the economic production quantity (EPQ) problem with backorder in which the setup cost, the holding cost and the backorder cost are characterized as fuzzy variables, respectively. Following expected value criterion and chance constrained criterion, a fuzzy expected value model (EVM) and a chance constrained programming (CCP) model are constructed. Then fuzzy simulat...