In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...

abstract: this paper presents dynamic modelling and control of a linear prismatic series of elastic actuator. the capability of generating large torques is why this actuator is increasingly used in human-assistive robotic systems. due to having human in the loop, the actuator requires precise control. a fractional pid controller known for its improved performance is used for the control, due to...

The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The classification is done using DGA data from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. PSO and GA were compared in terms of accu...

Meta-heuristics have already received considerable attention in various engineering optimization fields. As one of the most rewarding tasks, eigenvalue optimization of truss structures is concerned in this study. In the proposed problem formulation the fundamental eigenvalue is to be maximized for a constant structural weight. The optimum is searched using Particle Swarm Optimization, PSO and i...

in this paper, a new approach is proposed for the optimum design of single-phase induction motor. by using the classical design equations and the evolutionary algorithms such as genetic algorithms (ga), particle swarm optimization (pso) and modified particle swarm optimization (mpso), a single phase induction motor (spim) was designed with the maximum efficiency. the finite element method (fem)...

In this paper, well-known PSO algorithms reported in the literature for solving continuous function optimization problems were comparatively evaluated by considering real world data clustering problems. Data clustering problems are solved, by considering three performance clustering metrics such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The result...

In the present study, in order to predict the activity coefficient of inorganic ions, 12 cases of aqueous chloride solution were considered (AClx=1,2; A=Li, Na, K, Rb, Mg, Ca, Ba, Mn, Fe, Co, Ni). For this study, the UNIQUAC thermodynamic model is desired and its adjustable parameters are optimized with the Genetic + PSO algorithm. The optimization of the UNIQUAC model with PSO+ genetic algorit...

due to the fact that the error surface of adaptive infinite impulse response (iir) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. in this case, global optimization techniques are required in order to avoid the local minima. harmony search (hs), a musical inspired metaheuristic, is a recently ...

A hybrid particle swarm optimization (PSO) for multi-machine time scheduling problem (MTSP) with multicycles is proposed in this paper to choose the best starting time for each machine in each cycle under pre-described time window and a set of precedence machines for each machine; to minimize the total penalty cost. We developed hybrid algorithm by using a combination between PSO and Genetic Al...