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The paper introduces the principle of traditional PID algorithm, analyzes its advantages and disadvantages, and proposes a new control scheme — variable structure fuzzy neural network for such a nonlinear and complex system of automatic Gauge control (AGC). Variable structure fuzzy neural network combines the advantages of neural network and fuzzy control, and also adds a genetic algorithm to optimize...
This paper propose a new multi-objective model optimization allow training the multi-layer perceptron neural network (MLPNN) and optimizing its architecture. More precisely, this model aims to satisfy two objectives: the first one is minimizing the perceptron error (training objective) and the second one is minimizing the sum of the absolute weights (optimizing architecture objective). As known, a...
One of the important advantages of RFID technology is to identify multiple targets at the same time. However, in order to identify multi-object at the same time, it is necessary to solve the problem of improving the performance of tag reading. Among the factors affecting the performance of tag identification, the geometric distribution of multi-tag is the key one. With the advantage of GA-BP neural...
Genetic Algorithms are group of mathematical models in computational science by exciting evolution in AI techniques nowadays. These algorithms preserve critical information by applying data structure with simple chromosome recombination operators by encoding solution to a specific problem. Genetic algorithms they are optimizer, in which range of problems applied to it are quite broad. Genetic Algorithms...
Operating parameters and life parameters are the factors that determine the state of the engine. However, the operating parameters and life parameters also include many factors, such as cycle life damage rate, speed life damage rate, low and high pressure rotor rate and so on. Based on these factors, the evaluation and diagnosis system of aero-engine is built. Due to many factors that need to be considered,...
Following analyzing existing challenges in addressing the balance between exploration and exploitation encountered by evolutionary algorithms, this paper develops a Genetic Algorithm with speciation (GASP). It first incorporates a novel encoding scheme and recombination method for a balanced genetic divergence when locating global optima in complex applications, such as structural and dynamic design...
In this paper, a multilayer perceptron feed forward neural network (MPFNN) with good self-adapting and generalization ability is presented. The network is mainly used for solving the complex problems based on classification as well as regression. Most commonly, back propagation (BP) algorithm is used to train MPFNN, which suffers from the problem of local minima, over-fitting and poor convergence...
Wind power prediction technology is very important for scheduling of wind power farm. In order to improve the predictive accuracy of wind power, a short-term wind power prediction method based on genetic algorithm to optimize RBF neural network is proposed this paper. Genetic algorithm is used to optimize the weight, centers and widths of the hidden layer in RBF neural network. Relevant historical...
Classification is important task of data mining used to extract knowledge from huge volume of data. By nature, classification is multi-objective problem, as it required optimization of multiple objectives simultaneously like accuracy, sensitivity, squared error, precision etc. Traditionally, evolutionary algorithms were used to solve multi-objective classification problem by considering it as single-objective...
Power system stabilizers (PSS) works in conjunction with the excitation system to provide damping of the oscillation of synchronous machine. It has long been recognized as an effective means of curbing low frequency oscillations. Studies reveal that gain setting of the PSS needs to be restricted to a low value in order to ensure that none of the modes are adversely affected with the incorporation...
The parallel genetic algorithms implementation for neural networks models construction is discussed. The modification of this global optimization algorithm is proposed. The artificial neural networks are effective instrument to solve most problems of technological objectives and processes modelling. The article describes the aspects of genetic algorithms implementation for neural networks structure-parametric...
Infertility problem is an important issue in recent decades. Semen analysis is one of the principle tasks to evaluate male partner fertility potential. It has been seen in many researches that life habits and health status affect semen quality. Data mining as a decision support system can help to recognize this effect. The artificial neural network (ANN) is a powerful data mining tool that can be...
A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural Networks. This novel algorithm uses limited precision for both synaptic weights and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for the supervised training. The results are comparable or better than previously published work. The results are applicable to the realization of large-scale hardware neural...
The robustness issue in model-based diagnosis of process faults is addressed by means of artificial neural networks. The symptoms are generated by using observer schemes with dynamic neural nets. Their design is based on a hierarchical genetic algorithm, extended back-propagation method and multiobjective optimisation. The evolutionary search of genetic type is used to find the optimal architecture...
The paper presents a novel dynamic neural architecture that allows a flexible and compact representation of nonlinear processes. The suggested neural topology is obtained by providing local internal recurrence for the static neural network with complex weights. An evolutionary multiobjective design procedure assists the automatic selection of appropriate neural topologies and parameters. It searches...
The double inverted pendulum system is hard to reach steady state, the mathematical model before the simulation and physical control experiment. And the system requires higher performance to the controller. So the improved genetic algorithm is introduced into the wavelet neural network controller. The trained neural network has less number of iterations, smaller error and better global convergence...
Analysis of structural changes in the brain through magnetic resonance imaging can provide useful data for diagnosis and clinical supervision of patients through dementia. While the degree of sophistication reached by the MRI equipment is high, the quantification of tissue structures and has not yet been completely solved. Segmentations that these teams now allow those structures fail where the edges...
A method to predict the displacement of landside, genetic algorithm based on wavelet neural network (GAWNN) model, is presented in this paper. The hybrid model improves the predicting precision, which is compared with genetic algorithm based on back-propagation neural network (GABPNN). Furthermore, the hybrid model is applied for predicting the displacement of Baishuihe landslides in the Three Gorges...
Complex process modeling and optimization system is a hot area of research. A system model is proposed between process parameters and performance index by using the BP neural network for hydrogen cyanide (HCN) production process. In proposed method, the optimal process parameters would be searched by using genetic algorithm and these optimal parameters could be entered into BP neural network to predict...
Due to the strong global optimization capability and fast convergence, PSO has shown its efficiency in solving various real world benchmark applications. But premature convergence is one of the major drawback of PSO. In this paper to address this issue, a hybrid PSO-GA based Pi-sigma neural network with standard back propagation gradient descent learning (PSO-GA-PSNN) has been proposed for classification...
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