in this study, a multi-objective genetic algorithm (moga) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear multi-input multi-output (mimo) systems. in the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. furthermore, se- curing low-level and high-level ...

abstract: in this paper, a method for determination of refractive index in membrane of fuel cell on basis of three-longitudinal-mode laser heterodyne interferometer is presented. the optical path difference between the target and reference paths is fixed and phase shift is then calculated in terms of refractive index shift. the measurement accuracy of this system is limited by nonlinearity erro...

A modified genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The scheme is also applied to feedforward and recurrent neural networks.

This paper concerns the use of genetic algorithms for line labelling. We are interested in finding an optimal set of algorithm control parameters for this problem. We give results from using a simple genetic algorithm to solve several line labelling problems and discuss the effects of crossover type, population size, crossover rate, mutation rate and iteration limit on algorithm performance. We...

Evolutionary computing techniques were used to understand and model the time series observations of environmental parameters recorded by buoys on the ocean surface. The environmental parameters such as sea surface temperature, air temperature and surface wave height etc. are important in understanding the ocean characteristics and any suitable model that can forecast or predict the trend to any...

This paper presents an adaptive algorithm that can adjust parameters of genetic algorithm according to the observed performance. The parameter adaptation occurs in parallel to the running of genetic algorithm. The proposed method is compared with the algorithms that use random parameter sets and a standard parameter set. It is shown to be the most promising method from two performance measureme...

In biosorption research, a fairly broad range of mathematical models are used to correlate discrete data points obtained from batch equilibrium, batch kinetic or fixed bed breakthrough experiments. Most of these models are inherently nonlinear in their parameters. Some of the models have enjoyed widespread use, largely because they can be linearized to allow the estimation of parameters by leas...

The Levenberg-Marquardt (LM) minimization algorithm commonly employed in MOSFET model parameter extraction has several known deficiencies, such as poor convergence characteristics without a good initial guess, low likelihood of convergence to the globally optimal solution, and difficulty with simultaneous multiobjective optimizations. Furthermore, conventional tools require an expert user with ...

This is a learning note of genetic algorithm.

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...