Refining is an important process in pulping and paper-making industry. With both large uncertainties and rapid response, it is difficult to get accurate models of refining process. Developing efficient and practical c...
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Refining is an important process in pulping and paper-making industry. With both large uncertainties and rapid response, it is difficult to get accurate models of refining process. Developing efficient and practical control algorithms with robustness and adaptiveness for such a specific process is of essential benefit. In this paper, a hybrid controller is designed and implemented for "refining process optimal control system". It is demonstrated that the hybrid controller gives good performance. Nevertheless, It is very simple and available for practice.
An improved resource allocation network (RAN)-based online modelling method is proposed. RAN is a hidden layer network whose hidden units are Gaussian radial basis functions. The improved RAN can suppress the interfer...
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An improved resource allocation network (RAN)-based online modelling method is proposed. RAN is a hidden layer network whose hidden units are Gaussian radial basis functions. The improved RAN can suppress the interference of the new data on the weights of the previous training links. The influence of initial parameters on the network is thus described and the property values of these parameters and high approximation precision.
In this paper, we wish to find a minimal data size in order to better conceptualize industrial maintenance activities. We based our study on data given by a Synthetic Hidden Markov Model. This synthetic model is inten...
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In this paper, we wish to find a minimal data size in order to better conceptualize industrial maintenance activities. We based our study on data given by a Synthetic Hidden Markov Model. This synthetic model is intended to produce real industrial maintenance observations (or “symbols”), with a corresponding degradation indicator. These time series events are shown as Markov chains, also called “signatures”. The production of symbols is generated by using a uniform and a normal distribution. The evaluation is made by applying Shannon entropy on the HMM parameters. The results show a minimal number of data for each distribution studied. After a discussion about the use of a new “Sliding Window” of symbols usable in a Computerized Maintenance Management System, we developed two industrial applications and compare them with the best optimized “signature” previously found.
Automation of data processing of contactless diagnostics (detection) of the technical condition of the majority of nodes and aggregates of railway transport (RWT) minimizes the damage from failures of these systems in...
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Automation of data processing of contactless diagnostics (detection) of the technical condition of the majority of nodes and aggregates of railway transport (RWT) minimizes the damage from failures of these systems in operating modes. This becomes possible due to the rapid detection of serious defects at the stage of their origin. Basically, in practice, the control of the technical condition of the nodes and aggregates of the RWT is carried out during scheduled repairs. It is not always possible to identify incipient defects. Consequently, it is not always possible to warn personnel (machinists, repairmen, etc.) of significant damage to the RWT systems until their complete failure. The difficulties of obtaining diagnostic information is that there is interdependence between the main nodes of the RWT. This means that if physical damage occurs at any of the RWT nodes, in other nodes there can also occur malfunctions. As the main way to improve the efficiency of state detection of the nodes and aggregates of RWT, we see the direction of giving the adaptability property for an automated data processing system from various contactless diagnostic information removal systems. The global purpose can be achieved, in particular, through the use of machine learning methods and failure recognition (recognition objects). In order to improve the operational reliability and service life of the main nodes and aggregates of RWT, there are proposed an appropriate model and algorithm of machine learning of the operator control system of nodes and aggregates. It is proposed to use the Shannon normalized entropy measure and the Kullback-Leibler distance information criterion as a criterion of the learning effectiveness of the automated detection system and operator node state control of RWT. The article describes the application of the proposed method on the example of an automatic detection system (ADS) of the state of a traction motor of an electric locomotive. There are given the te
This ongoing work is vehemently dedicated to the investigation of a class of ordinary linear Volterra type integro-differential equations with fractional order in numerical mode. By replacing the unknown function by a...
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This ongoing work is vehemently dedicated to the investigation of a class of ordinary linear Volterra type integro-differential equations with fractional order in numerical mode. By replacing the unknown function by an appropriate multilayered feed-forward type neural structure, the fractional problem of such initial value is changed into a course of non-linear minimization equations, to some extent. Put differently, interest was sparked in structuring an optimized iterative first-order algorithm to estimate solutions for the origin fractional problem. On top of that, some computer simulation models exemplify the preciseness and well-functioning of the indicated iterative technique. The outstanding accomplished numerical outcomes conveniently reflect the productivity and competency of artificial neural network methods compared to customary approaches.
In this paper we implement six different learning algorithms in Optical Character Recognition (OCR) problem and achieve the criteria of end-time, number of iterations, train-set performance, test-set performance, vali...
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In this paper we implement six different learning algorithms in Optical Character Recognition (OCR) problem and achieve the criteria of end-time, number of iterations, train-set performance, test-set performance, validate-set performance and overall performance of these methods and compare them. Finally, we show the advantages and disadvantages of each method.
The learning algorithm for complex-bilinear recurrent neural network (C-BLRNN) derived in the paper referenced in the title is incorrect. The correct derivation, which follows the method in two other literatures, is p...
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The learning algorithm for complex-bilinear recurrent neural network (C-BLRNN) derived in the paper referenced in the title is incorrect. The correct derivation, which follows the method in two other literatures, is presented.
Get the benefits of Fuzzy control and Neural network control, put up a new quick learning control algorithm, to the controlled object which has movement characteristics and quickly varied parameters, obtains good cont...
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ISBN:
(纸本)7312012035
Get the benefits of Fuzzy control and Neural network control, put up a new quick learning control algorithm, to the controlled object which has movement characteristics and quickly varied parameters, obtains good control effects. In control solution space, use Fuzzy control to make primary partition quickly, then use Neural network control to get the precise control value in partition area. it gets successful application in electric touch quickly heated industrial process, and show its good control property and application value.
In this paper,the Wavelet Process Neural Network(WPNN) model is proposed based on the wavelet theory and the Process Neural Network(PNN) *** incorporates the neural network in learning from processes and the time-freq...
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In this paper,the Wavelet Process Neural Network(WPNN) model is proposed based on the wavelet theory and the Process Neural Network(PNN) *** incorporates the neural network in learning from processes and the time-frequency localization property of ***,the network can deal with continuous input signals,which make it facilitates in tackling dynamics of complex *** corresponding learning algorithm is given and the network is used to solve the problems of power load *** simulation test results indicate that the WPNN has a faster convergence speed and higher accuracy than the same scale *** provided an effective way for the problems of power load forecasting.
Considering that inputs of a process neural network(PNN) are generally time-varying functions while the inputs of many practical problems are discrete values of multiple series,in this paper,a process neural network w...
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Considering that inputs of a process neural network(PNN) are generally time-varying functions while the inputs of many practical problems are discrete values of multiple series,in this paper,a process neural network with discrete inputs is presented to provide improved forecasting results for solving the complex time series *** presented method first makes discrete input series carry out Walsh transformation,and submits the transformed series to the network for *** can solve the problem of space-time aggregation operation of *** order to examine the effectiveness of the presented method,the actual data of sunspots during 1749-2007 are *** predict the number of sunspots,the suitability of the developed model is examined in comparison with the other models to show its superiority and be an effective way of improving forecasting accuracy of networks.
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