This paper presents the design of an indirect adaptive feedback linearization (FBL)based control using dynamic neural networks (DNN) for full-car nonlinear electrohydraulic suspensions. Particle swarm optimization (PSO) algorithm is used in training the DNN to learn the dynamics of the system. A multi-loop, PSO-optimized proportional-integral-derivative (PID) control is implemented for the feed...

One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or...

This paper presents a novel method for solving optimal power quality monitor placement problem in monitoring voltage sags in power systems using the adaptive quantum-inspired particle swarm optimization (PSO). The optimization considers multi objective functions and handles observability constraint determined by the concept of the topological monitor reach area. The overall objective function c...

This paper introduces the application of particle swarm optimization techniques to generalized adaptive nonlinear and reeursive fdter structures. Particle swarm optimization (PSO) is a population based optimization algorithm, similar to the genetic algorithm (GA), that performs a struetored randomized search of an unknown parameter space by manipulating a population of parameter estimates to co...

This paper introduces the application of particle swarm optimization techniques to generalized adaptive nonlinear and recursive filter structures. Particle swarm optimization (PSO) is a population based optimization algorithm, similar to the genetic algorithm (GA), that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to c...

In this paper, we propose a fast 2-D block-based motion estimation algorithm called Particle Swarm Optimization Zero-motion Prejudgment(PSO-ZMP) which consists of three sequential routines: 1)Zero-motion prejudgment. The routine aims at finding static macroblocks(MB) which do not need to perform remaining search thus reduces the computational cost; 2)Predictive image coding and 3)PSO matching r...

Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjace...

Generally, the mechanical devices come with undesirable nonlinearities. Due to these nonlinearities the frequency domain system identification process in servo system seems to be a tough task. In the paper, particle swarm optimization (PSO) algorithm based hybrid technique is proposed for the frequency domain identification of servo system. The proposed hybrid technique is the combination of ar...

This paper presents Chemo-tactic PSO-DE (CPSO-DE) optimization algorithm combined with Lagrange Relaxation method (LR) for solving Unit Commitment (UC) problem. The proposed approach employs Chemo-tactic PSO-DE algorithm for optimal settings of Lagrange multipliers. It provides high-quality performance and reaches global solution and is a hybrid heuristic algorithm based on Bacterial Foraging O...