Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and ...
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Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace learning algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.
作者：
Wu, YTongji Univ
Dept Comp Sci & Engn Shanghai 200092 Peoples R China
The links from hidden layer to output layer are expanded for improving the learning performance of neural network. Based on this, a new neural network structure is proposed, and a learning algorithm is derived on it. ...
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ISBN:
(纸本)7121002159
The links from hidden layer to output layer are expanded for improving the learning performance of neural network. Based on this, a new neural network structure is proposed, and a learning algorithm is derived on it. And then, several n-parity, function approximation and pattern classification problem simulations are made to verify the effectiveness of the proposed method. The experimental results show that the proposed method has the dual merits of quick training speed and good generalization capability. It proves to be a very effective method.
Combined Cerebellar Model Articulation Controller neural network with Simple Adaptive Control, a kind of new control method, Cerebellar Model Articulation Controller Simple Adaptive Control is proposed, structures and...
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ISBN:
(纸本)9781424408276
Combined Cerebellar Model Articulation Controller neural network with Simple Adaptive Control, a kind of new control method, Cerebellar Model Articulation Controller Simple Adaptive Control is proposed, structures and learning algorithms of this control method are derived in this paper. In the design, fast learning of Cerebellar Model Articulation Controller neural network and simple structure of Simple Adaptive Control are combined. The simulation results show that the proposed method has fine accuracy, dynamic performance and robustness, and it is feasible and effective to be used to control high-order linear systems and. nonlinear systems.
Electric power system load forecasting plays an important role in the Energy Management System (EMS), which has great influence on the operation, controlling and planning of electric power system. A precise electric p...
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ISBN:
(纸本)9780769528755
Electric power system load forecasting plays an important role in the Energy Management System (EMS), which has great influence on the operation, controlling and planning of electric power system. A precise electric power system short term load forecasting will result in economic cost saving and improving security operation condition. With the development of deregulation in electric power system, the method of short term load forecasting with high accuracy is becoming more and more important. Due to the complicacy and uncertainty of load forecasting, electric power load is difficult to be forecasted precisely if no analysis model and numerical value algorithm model is applied, In order to improve the precision of electric power system short term load forecasting, a new load forecasting model is put foreword in this paper This paper presents a short-term load forecasting method using pattern recognition which obtains input sets belong to multi-layered fed-forward neural network, and artificial neural network in which BP learning algorithm is used to train samples. Load forecasting has become one of the major areas of research in electrical engineering in recent years. The artificial neural network used in short-time load forecasting can grasp interior rule in factors and complete complex mathematic mapping. Therefore, it is world wide applied effectively for power system short-term load forecasting.
A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro- Fuzzy Kolmogorov's Network (NFKN) with a " bottleneck" hidde...
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A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro- Fuzzy Kolmogorov's Network (NFKN) with a " bottleneck" hidden layer. Two training algorithms are considered. The validity of theoretical results and the advantages of the proposed model are confirmed by an experiment in nonlinear principal component analysis and an application in the visualization of high- dimensional wastewater treatment plant data.
In this paper we present learning algorithms for classes of categorial grammars restricted by negative constraints. We modify learning functions of Kanazawa [10] and apply them to these classes of grammars. We also pr...
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In this paper a method of creating layers of feed-forward neural network that does not need to be learned is presented. Described approach is based on algorithms used in synthesis of logic circuits. Experimental resul...
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ISBN:
(纸本)9783540734505
In this paper a method of creating layers of feed-forward neural network that does not need to be learned is presented. Described approach is based on algorithms used in synthesis of logic circuits. Experimental results presented in the paper prove that this method may significantly decrease the time of learning process, increase generalization ability and decrease a probability of sticking in a local minimum. Further work and goals to achieve are also discussed.
This paper proposes a novel model of support function machine (SFM) for time series predictions. Two machine learning models, namely, Support vector machines (SVM) and procedural neural networks (PNN) are compared in ...
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ISBN:
(纸本)9783540772255
This paper proposes a novel model of support function machine (SFM) for time series predictions. Two machine learning models, namely, Support vector machines (SVM) and procedural neural networks (PNN) are compared in solving time series and they inspire the creation of SFM. SFM aims to extend the support vectors to spatiotemporal domain, in which each component of vectors is a function with respect to time. In the view of the function, SFM transfers a vector function of time to a static vector. Similar to the SVM training procedure, the corresponding learning algorithm for SFM is presented, which is equivalent to solving a quadratic programming. Moreover, two practical examples are investigated and the experimental results illustrate the feasibility of SFM in modeling time series predictions.
A new approach of intelligent soft computing based on process neural network for variables estimate of process control system was proposed. Process neural network (PNN) is a new type of artificial neural network put f...
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ISBN:
(纸本)9783540742012
A new approach of intelligent soft computing based on process neural network for variables estimate of process control system was proposed. Process neural network (PNN) is a new type of artificial neural network put forward in recent years. Some algorithms of PNN were discussed, and convergence rate was comparatively low. An improved algorithm for raising training speed based on function orthogonal basis expansion in PNN for soft computing was researched. After increasing the normalizing rule on original algorithm, and introducing function momentum adjustment item and learning rate automatically adjustment method for network weight function, the training time of learning algorithm for PNN was reduced. The fact showed that the stability and training precision was improved with the learning rate automatic adjustment method, and it can also restrain the network falls into local least by introducing momentum adjustment item, and a good result of application in sewage disposal system was represented.
In this paper, a continuous wavelet process neural network (CWPNN) model is proposed based on the wavelet theory and process neural network model. The network offers good compromise between robust implementations resu...
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In this paper, a continuous wavelet process neural network (CWPNN) model is proposed based on the wavelet theory and process neural network model. The network offers good compromise between robust implementations resulting from the redundancy characteristic of non-orthogonal wavelets, and efficient functional representations that build on the time-frequency localization property of wavelets. Moreover, the network can deal with continuous input signals directly. The corresponding learning algorithm is given and the network is used to solve the problems of aeroengine condition monitoring. The simulation test results indicate that the CWPNN has a faster convergence speed and higher accuracy than the same scale process neural network (PNN) and BP neural network. This provided an effective way for the problems of aeroengine condition monitoring.
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