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Aiming at the problem that it is difficult to confirm the parameters of the PID controller and the parameters can not be changed once identified, an intelligent PID control method is proposed. According to the size of the system error, this algorithm controls the system with different subsections of different parameters, by using the particle swarm optimization (PSO) to optimize the parameters of...
This paper presents a novel algorithm for multiobjective training of Radial Basis Function (RBF) networks based on least-squares and Particle Swarm Optimization methods. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem, in which two conflicting objectives should be minimized. The objectives are related to the empirical training error...
A hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for Video OCR is presented in this paper. Video OCR is an important task towards enabling automatic content-based retrieval of digital video databases. However, since text is often displayed against a complex background, its detection and extraction is a challenging problem. In this paper, wavelet transformation is done...
Key parameters have great influence on particle swarm optimization (PSO) algorithm effect. Based on literature, that inertia weight's effect is more prominent, a new method dynamically adjust inertia weight is proposed to improve the performance of PSO. In improved method, the inertia weight is controlled by PSO optimization. The experiment results of functions indicate both the accuracy and precision...
This paper presents a trajectory planning method for saving the operating energy of a flexible manipulator in point-to-point (PTP) motion. An artificial neural network (ANN) is employed to generate the desired joint angle, and then, particle swarm optimization (PSO) is used as the learning algorithm. The sum of the motor torques is adopted as the objective function in the PSO algorithm. By operating...
Evaluation of certain properties of calcined alumina or special grade alumina is necessary and important to its manufactures. Generally it is determined in the laboratories using different instrumental and manual methods, which is cost and time intensive. In the present work, evolving neural network has been used for the estimation of a property given few others. To evolve the neural network model...
This paper contributes a novel Particle Swarm Optimization (PSO) method. The particle is updated not only by the best position in history (pbest) and the best position among all the particles in the swarm (gbest), but also using the position that is nearest neighbor of pbest. Additionally, we introduce a modified PSO algorithm based on the fuzzy clustering of particles to communication with the nearest...
The paper proposes a new hybrid forecasting model using auto regressive moving average (ARMA) as basic architecture and particle swarm optimization (PSO) as learning algorithm. These two combinations have yielded an efficient prediction model for retail sales volumes. To facilitate comparison ARMA, functional link artificial neural network (FLANN) and MLP models are also simulated. The performance...
In this paper, a particle swarm optimization (PSO) based camera calibration approach is presented to determine the external and internal calibration parameters from the knowledge of a given set of points in object space. First, the image formation model for a pinhole camera is formulated in terms of a feed-forward neural network (NN) and then this neural network is trained using particle swarm optimization...
Several strategies have been proposed to provide quality solutions to the unit commitment problem and increase the potential saving in the power system operation. These include deterministic and stochastic search algorithms. One of the limitations of deterministic approaches is, they suffer from the curse of dimensionality when dealing with the modern power system with large number of generators....
This work presents system identification using neural network approaches for modelling a laboratory based twin rotor multi-input multi-output system (TRMS). Here we focus on a memetic algorithm based approach for training the multilayer perceptron neural network (NN) applied to nonlinear system identification. In the proposed system identification scheme, we have exploited three global search methods...
This paper utilizes particle swarm optimization method to train the weights of neural network (NN). Particle swarm optimization (PSO) is a population based stochastic optimization technique. Unlike genetic algorithm (GA), PSO has no evolution operators such as crossover and mutation. Compared to GA, the advantages of PSO are that it is easy to implement and there are few parameters to adjust. In this...
This paper explore the swarm intelligence stability based on stochastic diffusion search (SDS) which is capable to find rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of the positive feedback...
In this paper, the feasibility of using probabilistic causal-effect model is studied and we apply it in particle swarm optimization algorithm (PSO) to classify the faults of mine hoist. In order to enhance the PSO performance, we propose the probability function to nonlinearly map the data into a feature space in probabilistic causal-effect model, and with it, fault diagnosis is simplified into optimization...
A novel model was proposed for short-term electricity price forecasting based on rough set approach and improved support vector machines (SVM). Firstly, we can get reduced information table with no information loss by rough set approach. And then, this reduced information is used to develop classification rules and train SVM, at the same time, we make use of the particle swarm optimization to optimize...
Electricity demand forecasting is an important index to make power development plan and dispatch the loading of generating units in order to meet system demand. In order to improve the accuracy of the forecasting, we apply the feedforward neural network for electricity demand forecasting. Inspired by the idea of artificial fish swarm algorithm, in this paper we proposed one hybrid evolutionary algorithm...
A recurrent wavelet neural network (RWNN) controller is proposed in this study to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, an RWNN controller is proposed to control the PMLSM. Moreover, the connective weights, translations and dilations of the RWNN...
In the present study, an expert system is developed to identify and classify fault condition for diesel engine. Vibration signals are collected on a diesel engine test platform. Wavelet packet analysis (WPA) coefficients of vibration signals are used for evaluating their Shannon entropy and treated as the features to identify the fault conditions of diesel engine in the preprocessing. A back-propagation...
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