"Design of Variable Structure Stabilizer for a Nonlinesr Mode of SMIB System: Particle Swarm Approach"

In this paper, a particle swarm-(PSO) based variable structure stabilizer (VSC) is proposed for enhancing the dynamic stability of a nonlinear model of synchronous machine infinite busbar system (SMIB). Unlike the methods reported in the literature which involve either linearizing the model of synchronous machine around a suitable operation point or applying nonlinear transformation techniques before linear control theory is used in designing a fixed parameter PSS, the present method formulates the design of VSC as an optimization problem and utilized the PSO algorithm to provide a simple and systematic way of arriving at the optimal feedback gains and switching vector values of the stabilizer. When compared to previous methods, simulation results showed the effectiveness of the proposed stabilizer design

"Particle swarm based design of variable structure stabilizer for a nonlinear model of SMIB system"

In this paper, a particle swarm-(PSO) based variable structure stabilizer (VSC) is proposed for enhancing the dynamic stability of a nonlinear model of synchronous machine infinite busbar system (SMIB). Unlike the methods reported in the literature which involve either linearizing the model of synchronous machine around a suitable operation point or applying nonlinear transformation techniques before linear control theory is used in designing a fixed parameter PSS, the present method formulates the design of VSC as an optimization problem and utilized the PSO algorithm to provide a simple and systematic way of arriving at the optimal feedback gains and switching vector values of the stabilizer. When compared to previous methods, simulation results showed the effectiveness of the proposed stabilizer design

"Design of Variable Structure Stabilizer for a Nonlinesr Mode of SMIB System: Particle Swarm Approach"

In this paper, a particle swarm-(PSO) based variable structure stabilizer (VSC) is proposed for enhancing the dynamic stability of a nonlinear model of synchronous machine infinite busbar system (SMIB). Unlike the methods reported in the literature which involve either linearizing the model of synchronous machine around a suitable operation point or applying nonlinear transformation techniques before linear control theory is used in designing a fixed parameter PSS, the present method formulates the design of VSC as an optimization problem and utilized the PSO algorithm to provide a simple and systematic way of arriving at the optimal feedback gains and switching vector values of the stabilizer. When compared to previous methods, simulation results showed the effectiveness of the proposed stabilizer design

“Adaptive Control Technique Using Multilayer Feedforward Neural Networks”

This paper presents a new method for implementing adaptive controllers using multilayer feedforward neural networks (MFNN). The controlled process is approximated at each sampling time by a linear time-invariant (LTI) model. The proposed adaptive controller is a combination of a parameter estimation algorithm to estimate the parameters of the process and an adaptation algorithm for the connection weights of the neural network. An adaptation algorithm to adjust the connection weights of the neural network has been derived. Simulation results are included to demonstrate the feasibility and the adaptive properties of the proposed controller

"Particle swarm based design of variable structure stabilizer for a nonlinear model of SMIB system"

In this paper, a particle swarm-(PSO) based variable structure stabilizer (VSC) is proposed for enhancing the dynamic stability of a nonlinear model of synchronous machine infinite busbar system (SMIB). Unlike the methods reported in the literature which involve either linearizing the model of synchronous machine around a suitable operation point or applying nonlinear transformation techniques before linear control theory is used in designing a fixed parameter PSS, the present method formulates the design of VSC as an optimization problem and utilized the PSO algorithm to provide a simple and systematic way of arriving at the optimal feedback gains and switching vector values of the stabilizer. When compared to previous methods, simulation results showed the effectiveness of the proposed stabilizer design

"Design of Variable Structure Stabilizer for a Nonlinesr Mode of SMIB System: Particle Swarm Approach"

In this paper, a particle swarm-(PSO) based variable structure stabilizer (VSC) is proposed for enhancing the dynamic stability of a nonlinear model of synchronous machine infinite busbar system (SMIB). Unlike the methods reported in the literature which involve either linearizing the model of synchronous machine around a suitable operation point or applying nonlinear transformation techniques before linear control theory is used in designing a fixed parameter PSS, the present method formulates the design of VSC as an optimization problem and utilized the PSO algorithm to provide a simple and systematic way of arriving at the optimal feedback gains and switching vector values of the stabilizer. When compared to previous methods, simulation results showed the effectiveness of the proposed stabilizer design

“Adaptive Control Technique Using Multilayer Feedforward Neural Networks”

This paper presents a new method for implementing adaptive controllers using multilayer feedforward neural networks (MFNN). The controlled process is approximated at each sampling time by a linear time-invariant (LTI) model. The proposed adaptive controller is a combination of a parameter estimation algorithm to estimate the parameters of the process and an adaptation algorithm for the connection weights of the neural network. An adaptation algorithm to adjust the connection weights of the neural network has been derived. Simulation results are included to demonstrate the feasibility and the adaptive properties of the proposed controller

Use of multilayer feedforward neural networks in identification and control of Wiener model

Includes bibliographical references (page 258).The problem of identification and control of a Wiener model is studied. The proposed identification model uses a hybrid model consisting of a linear autoregressive moving average model in cascade with a multilayer feed forward neural network. A two-step procedure is proposed to estimate the linear and nonlinear parts separately. Control of the Wiener model can be achieved by inserting the inverse of the static nonlinearity in the appropriate loop locations. Simulation results illustrate the performance of the proposed method

Use of multilayer feedforward neural networks in identification andcontrol of Wiener model

The problem of identification and control of a Wiener model is studied. The proposed identification model uses a hybrid model consisting of a linear autoregressive moving average model in cascade with a multilayer feedforward neural network. A two-step procedure is proposed to estimate the linear and nonlinear parts separately. Control of the Wiener model can be achieved by inserting the inverse of the static nonlinearity in the appropriate loop locations. Simulation results illustrate the performance of the proposed metho

Competitive learning/reflected residual vector quantization for coding angiogram images

Medical images need to be compressed for the purpose of storage/transmission of a large volume of medical data. Reflected residual vector quantization (RRVQ) has emerged recently as one of the computationally cheap compression algorithms. RRVQ, which is a lossy compression scheme, was introduced as an alternative design algorithm for residual vector quantization (RVQ) structure (a structure famous for providing progressive quantization). However, RRVQ is not guaranteed to reach global minimum. It was found that it has a higher probability to diverge when used with nonGaussian and nonLaplacian image sources such as angiogram images. By employing competitive learning neural network in the codebook design process, we tried to obtain a stable and convergent algorithm. This paper deals with employing competitive learning neural network in RRVQ design algorithm that results in competitive learning RRVQ algorithm for the RVQ structure. Simulation results indicate that the new proposed algorithm is indeed convergent with high probability and provides peak signal-to-noise ratio (PSNR) of approximately 32 dB for an-giogram images at an average encoding bit rate of 0.25 bits per pixel