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RISSystem identification and control using adaptive particle swarm optimization
This paper presents a methodology for finding optimal system parameters and optimal control parameters using a novel adaptive particle swarm optimization (APSO) algorithm. In the proposed APSO, every particle dynamically ...
A general insight into the effect of neuron structure on classification
This paper gives a general insight into how the neuron structure in a multilayer
perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the
common neuron structures are based ...
A Novel Adaptive Neural Sliding Mode Control for Systems with Unknown Dynamics
Abstract— In this paper, an adaptive neural sliding mode controller (ANSMC) is proposed as an asymptotically stable robust controller for a class of Control Affine Nonlinear Systems (CANSs) with unknown dynamics. In the ...
Solving nonlinear optimal control problems using a hybrid IPSO–SQP algorithm
A hybrid algorithm by integrating an improved particle swarm optimization (IPSO) with successive quadratic programming (SQP), namely IPSO–SQP, is proposed for solving nonlinear optimal control problems. The particle swarm ...
Parameter estimation of bilinear systems based on an adaptive particle
Bilinear models can approximate a large class of nonlinear systems adequately and usually with
considerable parsimony in the number of coefficients required. This paper presents the application of
Particle ...
adaptive optimal control of unknown constrained-input system using policy iteration and neural networks
This paper presents an online policy iteration (PI)
algorithm to learn the continuous-time optimal control solution
for unknown constrained-input systems. The proposed PI algorithm
is implemented on ...
Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems
In this paper, an integral reinforcement learning (IRL) algorithm on an actor–critic structure is developed
to learn online the solution to the Hamilton–Jacobi–Bellman equation for partially-unknown constrainedinp ...
A policy iteration approach to online optimal control of continuous-time constrained-input systems
This paper is an effort towards developing an online learning algorithm to find the optimal control
solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on
the ...
Modeling and optimization of high chromium alloy wear in phosphate laboratory grinding mill with fuzzy logic and particle swarm optimization technique
This study evaluated the potential of fuzzy logic as an alternative method to the traditional statistical
regression techniques, which were employed in a previous publication (Chen et al., 2006), for predicting
the ...
Employing Adaptive Particle Swarm Optimization Algorithm for Parameter Estimation of an Exciter Machine
Winding inductances of an exciter machine of brushless generator normally consist of nonsinusoidal terms versus rotor position angle, so evaluations of the inductances necessitate detailed modeling and complicated parameter ...