The artificial neural networks (ANN) which have broad application were proposed to develop multiphase ceramie cutting tool materials. Based on the back propagation algorithm of the forward multilayer perceptron, the m...
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The artificial neural networks (ANN) which have broad application were proposed to develop multiphase ceramie cutting tool materials. Based on the back propagation algorithm of the forward multilayer perceptron, the models to predict volume content of composition in particie reinforced ceramies are established. The Al2O3/TiN ceramie cutting tool material was developed by ANN, whose mechanicai properties fully satisfy the cutting requirements.
It’s very important to control the electrode current of arc *** paper firstly discuss intelligent control method of arc furnace based on neural network,then the three-phase current prediction model of arc furnace has...
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It’s very important to control the electrode current of arc *** paper firstly discuss intelligent control method of arc furnace based on neural network,then the three-phase current prediction model of arc furnace has been built and it is analyzed and simulated by matlab *** result shows that the electrode is effective controlled and the effect by using improved bp method is satisfactory.
It is a common method to handle with nonlinear problems by linear models. While the parameter estimation achieved by this method is biased, the precision of its curve fitting is low. In this paper, we adopt the techni...
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It is a common method to handle with nonlinear problems by linear models. While the parameter estimation achieved by this method is biased, the precision of its curve fitting is low. In this paper, we adopt the technique of artificial neural network (ANN) to process nonlinear regression analysis and seek for the solution of analyzing dynamic state of groundwater level. The result is satisfactory.
Artificial neural networks (ANNs) was developed very quickly and applied very widely in recent years due to its strong ability to solve the nonlinear problems. The artificial neural network-based method was also widel...
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Artificial neural networks (ANNs) was developed very quickly and applied very widely in recent years due to its strong ability to solve the nonlinear problems. The artificial neural network-based method was also widely applied to the geotechnical engineering. The complexity of the geotechnical engineering problems because of the strong nonlinear relationship between knows and unknowns of the problems can be mapped very well by artificial neural networks. Researches on the application of artificial neural network in geotechnical engineering are reviewed and appraised in this paper. All the networks mentioned are trained with the back-propagation algorithm which is widely used by a great number of researchers. Research reveals that the method is feasible and it will be interested for more geotechnical engineers.
This paper summarizes the features and principles of artificial neural network,and problems existing in the traditional expert *** it introduces the structural components and advantages of fault diagnosis system which...
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ISBN:
(纸本)9781479919819
This paper summarizes the features and principles of artificial neural network,and problems existing in the traditional expert *** it introduces the structural components and advantages of fault diagnosis system which combined with artificial neural network and expert *** it elaborates the basic principle and implementation process of fault diagnosis expert system based on bp neural network.
A trajectory recognition and simulation technique based on PSO (particle swarm optimizer) is proposed. According to trajectory centric motion equations, and taking CMAC neural network as its core, a trajectory recogni...
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A trajectory recognition and simulation technique based on PSO (particle swarm optimizer) is proposed. According to trajectory centric motion equations, and taking CMAC neural network as its core, a trajectory recognition network is built up. The PSO algorithm controls the realization of recognition and simulation. Simulation results reveal that the proposed recognition technique based on PSO has higher precision of recognition and better convergence than those based on bp algorithm.
The backpropagation (bp) algorithm is a learning algorithm for the multilayer perceptron. However, when this algorithm is applied to the pattern classification problem the generalization ability may not be maximized, ...
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The backpropagation (bp) algorithm is a learning algorithm for the multilayer perceptron. However, when this algorithm is applied to the pattern classification problem the generalization ability may not be maximized, even if the learning converges. This paper proposes an algorithm to improve the generalization ability by shifting the hyper-plane constructed by the bp algorithm on the basis of internal information. The basic properties of the algorithm are analyzed. Then, the algorithm is extended to the multi layer perceptron and its generalization ability is examined by computer simulation. The proposed method is shown to be useful when the training data are sparse. (C) 2004 Wiley Periodicals, Inc.
A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluct...
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A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than bp algorithm and the second order learning algorithm that was given by Karayiannis.
Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a bp neural network （call...
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Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a bp neural network （called the H-bp algorithm） for compensating function model errors is put forward. The function model is assumed as y =f（x1, x2,… ,xn）, and the special structure of the H-bp algorithm is determined as （ n ＋ 1） ×p × 1, where （n ＋ 1） is the element number of the input layer, and the elements are xl, x2,…, xn and y＇ （ y＇ is the value calculated by the function model）; p is the element number of the hidden layer, and it is usually determined after many tests; 1 is the dement number of the output layer, and the element is △y = y0-y＇（y0 is the known value of the sample）. The calculation steps of the H-bp algorithm are introduced in detail. And then, the results of three methods for compensating function model errors from one engineering project are compared with each other. After being compensated, the accuracy of the traditional methods is about ± 19 mm, and the accuracy of the H-bp algorithm is ± 4. 3 mm. It shows that the proposed method based on a neural network is more effective than traditional methods for compensating function model errors.
Sensors in the primary circuit of a pressurized water reactor (PWR) are normally designed with redundant structures to improve system safety and reliability. However, reliability of the actual system is often lower th...
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Sensors in the primary circuit of a pressurized water reactor (PWR) are normally designed with redundant structures to improve system safety and reliability. However, reliability of the actual system is often lower than that obtained by theoretical calculation due to the inevitable occurrence of common mode fault (CMF), which is a dependent failure event that can cause multiple failures in redundant channels. CMF may increase the reliability deviation of the system by orders of magnitude and, hence, seriously affects the reliability of the system. To mitigate the CMF of redundant sensors in nuclear power plants, an artificial neural network (ANN) can serve as a data-driven analytic model to monitor sensor parameters, to identify any possible abnormal status of the sensors, and provide an early warning. In this study, by using the high-fidelity dataset obtained in a full-scope PWR simulator as training, validation, and test data, a relevant parameter-based ANN black-box model (RPANN) was established by employing the back-propagation (bp) learning algorithm, which was then defined as an analytic redundancy. Time series-based ANN checking models (TSANNs) were also established for each of the input and output parameters of the RPANN in order to identify its abnormal state based on historical data in the past. When combined with the existing hardware redundancy, the ANN-based analytic redundancy can serve as an online monitoring tool of the hardware status and an online diagnosis strategy for sensor faults. Furthermore, ANN-based analytic redundancy can replace faulty hardware sensors to analytically reconstruct the reading of the monitored sensor parameter without having to reduce the reactor output power or even shut down the reactor for emergency maintenance so that the on-site calibration frequency of hardware sensors in redundant channels can be effectively reduced. This is not only of vital importance in reducing operation and maintenance costs of existing PWR powe
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