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This paper investigates the use of genetic algorithms in the identification of Wiener model. The parameters describing the linear system and the static nonlinearity are estimated from input-output measurements by minimizing the error between the actual and identified systems. Using genetic algorithms, systems with non-minimum phase characteristics can be identified. Simulation results reveal the effectiveness...
A new technique, based on differential evolution algorithm, is proposed for solving the parameter identification problem of nonlinear systems. The technique improves the accuracy of parameter identification. Two kinds of process systems have been used as examples for demonstration. The effectiveness of differential evolution algorithm is compared with that of genetic algorithms in terms of obtained...
The main objective of this work is to develop a cost effective off-line method for determination of induction motor equivalent circuit parameters by conducting a single load test on the motor. The proposed scheme is an alternative viable method to conventional means of no-load and blocked rotor tests. The identification of motor parameters is redrafted as a multi-objective optimization problem and...
This paper presents a method for the identification of the fault parameters of hybrid systems with unknown mode changes after fault occurring. The identification method utilizes genetic algorithm (GA) to identify fault parameters and unknown mode changes simultaneously based on global analytical redundancy relation (GARR). Fault parameters and mode change time of all switches are encoded into one...
An efficient and simple method of parameter identification for semiconductor devices is presented in this paper. The method is based on improved particle swarm optimization (IPSO), which can overcome some deficiencies of standard PSO. A typical semiconductor device, Schottky-barrier diode (SBD), has been used as an example for demonstration. The performance of the IPSO was compared with the standard...
In this paper, we propose a modified genetic algorithm (MGA) with calibrating fitness functions, weighted bit mutation, and rebuilding mechanism for the parameter estimation of software reliability growth models (SRGMs). An example using a real failure data is given to demonstrate the performance of proposed method. Experimental result shows that MGA is effective for estimating the parameters of SRGM.
This paper investigates a new technique for estimating the shape parameter of a K-distribution based on fuzzy neural network (FNN). In order to improve the estimation accuracy with inexpensive computational requirement, the FNN estimator is used to accurate the solutions of the nonlinear equations and the inverse functions (gk(nucirc))of the Raghavanpsilas and ML/MOM (Maximum-Likelihood and Method...
This work describes GASpeech: a framework centered on genetic algorithms for automatically estimating the input parameters of Klatt's speech synthesizer. GASpeech aims to speed up the process of speech imitation (or utterance copy), where one has to find the model parameters that lead to a synthesized speech sounding close enough to the natural target speech (i.e. low spectral distortion). The architecture...
This paper describes a genetic algorithm based approach to detect and predict high-impact events. While, these events occur infrequently, they are quite costly, meaning that they have a high-impact on the system key performance indicators. This approach is based on mining for these events and subsequences that are predictive of these high-impact events from historical data and then classifying these...
In hydroscience investigations, there are many hydraulic parameters to need identifying by use of optimization methods. According to dasiaNatural Selectionpsila from Darwinism, Genetic Algorithms (GA) has developed rapidly as effective and much robust optimization technique in recent ten years. But it isnpsilat easily applied to practice for Simple Genetic Algorithms (SGA) has the disadvantages of...
This paper describes a novel approach for identifying induction motor electrical parameters in function of flux levels based on experimental transient measurements from a vector controlled induction motor (I.M.) drive and using an off line genetic algorithm (GA) routine with a linear machine model. The evaluation of the electrical motor parameters is achieved by minimizing the error between experimental...
An effective technique for estimating parameters, losses, shaft power and efficiency of three-phase induction motors in field conditions, is proposed in this paper. It is focused on the determination of the operating efficiency of motors under unbalanced voltages or deviated voltage and frequency without caring out special tests, removing the motors, or measuring the output power or torque. Only few...
A method to identify the degradation parameter of the defocused blur image based on genetic algorithm is proposed. In this technique, a FFT transform is performed in the frequency domain to estimate the range of defocus parameter, and then genetic algorithm is used to search in this domain to find the accurate value of the radius. We experimentally illustrate its performance; results show that the...
The frequency modulation sound parameter identification is a complex multimodal optimization problem. In this paper, we proposed four evolutionary hybrid algorithms to solve this problem. First we combine genetic algorithm (GA) and queen-bee algorithm (QB) with a random optimization method (RO) and generate memetic and QB-memetic hybrid algorithms, respectively; then modified Nelder-Mead simplex algorithm...
This paper presents a new technique for induction motor parameter identification. The proposed technique is based on a simple startup test using a standard V/F inverter. The recorded startup currents are compared to that obtained by simulation of an induction motor model. A Modified PSO optimization is used to find out the best model parameter that minimizes the sum square error between the measured...
This paper introduces the application of particle swarm optimization (PSO) technique to identify the parameters of pole-zero plants or infinite impulse response (IIR) systems. The PSO is one of the evolutionary computing tools that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge to a suitable solution with low computational...
Estimation of parameters from kinetic model of batch fermentation is a tough searching problem. Unfortunately, the traditional approaches easily get stuck in a local minimum. So particle swarm optimization (QPSO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm were used to estimate parameters from kinetic model of batch fermentation in this paper. The result compared with...
This paper addresses a novel approach based on neuro-fuzzy inference system to solve the estimation problem of the K-distributed parameters. The method is based on a network implementation with real weights and the real genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. The proposed FNN estimator is based on the arithmetic and...
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