This paper proposes an Enhanced Particle Swarm Optimization (EPSO) for extracting optimal rule set and tuning membership function for fuzzy logic based classifier model. The standard PSO is more sensitive to premature convergence due to lack of diversity in the swarm and can easily get trapped into local minima when it is used for data classification. To overcome this issue, BLX-α crossover and...

This paper presents the optimal fuzzy controller design for load frequency control (LFC) of two area power system using Adaptive Particle Swarm Optimization (APSO). Firstly, the controller is designed according to Fuzzy Logic rules such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC) used earlier was optimized with PSO so as to obtain optimal adjustment of ...

the design and application of a fuzzy logic based controller to a leakage current relay waspresented in this paper. a novel approach based on fuzzy logic to form the tripping action of leakagecurrent relay was investigated. in addition to the theoretical aspect of fuzzy logic, detection of a leakagecurrent, measurement of the leakage current, determination of its duration, trip opening of fault...

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...

Particle swarm optimization is a population based optimization technique that is based on probability rules. In this technique each particle moves toward their best individual and group experience had occurred. Fundamental problems of standard PSO algorithm are the falling into the trap of local optimum and its low speed of convergence. One approach for solving the above problems is to combine ...

In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimizes the deviation (error term) between the decisions of the fuzzy logic systems and the decisions of experts. A range of approaches – such as genetic algorithms (GA), particle swarm optimization (PSO), artificial neural networks (ANN), and adaptive network based fuzzy inference systems (ANFIS) – can ...

Fuzzy logic adds to bivalent logic an important capability—a capability to reason precisely with imperfect information. Imperfect information is information which in one or more respects is imprecise, uncertain, incomplete, unreliable, vague or partially true. In fuzzy logic, results of reasoning are expected to be provably valid, or p-valid for short. Extended fuzzy logic adds an equally impor...

In this paper the design and development of a hierarchical fuzzy logic Systems are investigated. A new method using genetic algorithms for design of hierarchical fuzzy logic systems are proposed. This research study is unique in the way proposed method is applied to design and development of hierarchical fuzzy logic systems. The proposed method is then applied to financial modelling and predict...

It has proposed a kind of hybrid method based on intuitionistic fuzzy set theory and particle swarm optimization (PSO) algorithm-based neural network (NN). We apply it to the integrative damage effect assessment of battlefield target. Firstly, we improve PSO algorithm, propose the adaptive inertia factor and excellence selection mechanism, introduce inter-partition particle swarm initialization...