15 Result(s)
within
Computation by Abstract Devices
Wen-Yu **
Conference Paper
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Chapter and Conference Paper
Selective Ensemble Modeling Parameters of Mill Load Based on Shell Vibration Signal
Load parameters inside the ball mill have direct relationships with the optimal operation of grinding process. This paper aims to develop a selective ensemble modeling approach to estimate these parameters. At...
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Chapter and Conference Paper
On-Line Modeling Via Fuzzy Support Vector Machines
This paper describes a novel nonlinear modeling approach by on-line clustering, fuzzy rules and support vector machine. Structure identification is realized by an on-line clustering method and fuzzy support ve...
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Chapter and Conference Paper
Neural Networks Training with Optimal Bounded Ellipsoid Algorithm
Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Ka...
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Chapter and Conference Paper
Recurrent Fuzzy CMAC for Nonlinear System Modeling
Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension i...
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Chapter and Conference Paper
Integrated Analytic Framework for Neural Network Construction
This paper investigates the construction of a wide class of singlehidden layer neural networks (SLNNs) with or without tunable parameters in the hidden nodes. It is a challenging problem if both the parameter ...
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Chapter and Conference Paper
PD Control of Overhead Crane Systems with Neural Compensation
This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compen...
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Chapter and Conference Paper
Passivity Analysis for Neuro Identifier with Different Time-Scales
Many physical systems contains fast and slow phenomenons. In this paper we propose a dynamic neural networks with different time-scales to model the nonlinear system. Passivity-based approach is used to derive...
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Chapter and Conference Paper
System Identification Using Hierarchical Fuzzy CMAC Neural Networks
The conventional fuzzy CMAC can be viewed as a basis function network with supervised learning, and performs well in terms of its fast learning speed and local generalization capability for approximating nonli...
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Chapter and Conference Paper
Training Cellular Neural Networks with Stable Learning Algorithm
In this paper we propose a new stable learning algorithm for Cellular Neural Networks. Our approach is based on the input-to-state stability theory, so to obtain learning laws that do not need robust modificat...
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Chapter and Conference Paper
Passivity Analysis of Dynamic Neural Networks with Different Time-Scales
Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed network be stable. In this paper, the ...
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Chapter and Conference Paper
Discrete-Time Sliding-Mode Control Based on Neural Networks
In this paper, we present a new sliding mode controller for a class of unknown nonlinear discrete-time systems. We make the following two modifications: 1) The neural identifier which is used to estimate the u...
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Chapter and Conference Paper
Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets
Support vector machine (SVM) has been successfully applied to solve a large number of classification problems. Despite its good theoretic foundations and good capability of generalization, it is a big challeng...
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Chapter and Conference Paper
A Consumer Interest Prediction System from Transaction Behaviors in Electronic Commerce
Consumer interest prediction usually uses transaction behaviors for predicting consumer’s goal and interested items. The correct prediction heavily depends on the complete information of user profiles. The pre...
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Chapter and Conference Paper
System Identification Using Adjustable RBF Neural Network with Stable Learning Algorithms
In general, RBF neural network cannot match nonlinear systems exactly. Unmodeled dynamic leads parameters drift and even instability problem. According to system identification theory, robust modification term...
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Chapter and Conference Paper
Robust Adaptive Control Using Neural Networks and Projection
By using differential neural networks, we present a novel robust adaptive controller for a class of unknown nonlinear systems. First, dead-zone and projection techniques are applied to neural model, such that ...
