Gelenbe has modeled neural networks using an analogy with queuing theory. This model (called Random Neural Network) calculates the probability of activation of the neurons in the network. Recently, Fourneau and Gelenb...
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Gelenbe has modeled neural networks using an analogy with queuing theory. This model (called Random Neural Network) calculates the probability of activation of the neurons in the network. Recently, Fourneau and Gelenbe have proposed an extension of this model, called multiple classes random neural network model. The purpose of this paper is to describe the use of the multiple classes random neural network model to learn patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon non-linear equations of the multiple classes random neural network model using gradient descent of a quadratic error function. Ttl addition, we propose a progressive retrieval process with adaptive threshold values. The experimental evaluation shows that the learning algorithm provides good results.
The real-time fault detection and diagnosis are critical for healthy operation of electromechanical systems, of which the complex characteristics affect the performance of current shop floor fault diagnosis methods. A...
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ISBN:
(纸本)0780382323
The real-time fault detection and diagnosis are critical for healthy operation of electromechanical systems, of which the complex characteristics affect the performance of current shop floor fault diagnosis methods. Aiming to overcome the drawbacks, this paper presents a new fault diagnosis method using a newly developed method, support vector machines (SVM). First, the basic theory of SVM is briefly introduced and new intelligent fault diagnosis system is presented. Next, three common SVM algorithms - v-SV, Lagrangian, and Hyper-kernel - are employed for the proposed multiple faults diagnosis system. In comparison, the trade-offs among these three methods are discussed resulting in a general guideline of selecting appropriate learning algorithm for various applications. Then, the methods are applied for diagnosing vibration signals of a typical electromechanical system, elevator door. The real-time tests on 10 faulty conditions demonstrate that the proposed method is effectively and efficiently. In addition, the feature of only few training samples required and fast in calculation allow the method have a big potential in real-world applications.
This paper studies a property of neural network architecture for non-linear modeling. This method was proposed in our previous work and has three improvements;1) the design of a sigmoidal function with localized deriv...
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ISBN:
(纸本)4907764227
This paper studies a property of neural network architecture for non-linear modeling. This method was proposed in our previous work and has three improvements;1) the design of a sigmoidal function with localized derivative, 2) a deterministic scheme for weight initialization, and 3) an updating rule for weight parameters. We discuss its robustness against noise based on simulation results.
Two principles of neurocomputational design are implemented into an autonomous real-world device, such as a helicopter. The helicopter has a motivational component towards emitting motor responses in a manner similar ...
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Based on the principle of Radial Basis Function (RBF) Neural Network, a learning method is presented for the identification of a complex system model. The RBF algorithm is employed on the learning and identifying proc...
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ISBN:
(纸本)3540228438
Based on the principle of Radial Basis Function (RBF) Neural Network, a learning method is presented for the identification of a complex system model. The RBF algorithm is employed on the learning and identifying process of the nonlinear model. The simulation results show that the presented method has good effect on speeding up the learning and approaching process of the nonlinear complex model, and has an excellent performance on learning convergence.
The purpose of this thesis was to design and evaluate The ContexTable, a context-aware system built into a kitchen table. After establishing the current status of the field of context-aware systems and the hurdles and...
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The purpose of this thesis was to design and evaluate The ContexTable, a context-aware system built into a kitchen table. After establishing the current status of the field of context-aware systems and the hurdles and problems being faced, a functioning prototype system was designed and built. The prototype makes it possible to explore established, untested theory and novel solutions to problems faced in the field.
In this paper, we propose a new learning algorithm for multilayer feedforward neural networks, which converges faster and achieves a better classification accuracy than the conventional backpropagation learning algori...
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In this paper, we propose a new learning algorithm for multilayer feedforward neural networks, which converges faster and achieves a better classification accuracy than the conventional backpropagation learning algorithm for pattern classification. In the conventional backpropagation learning algorithm, weights are adjusted to reduce the error or cost function that reflects the differences between the computed and the desired outputs. In the proposed learning algorithm, we view each term of the output layer as a function of weights and adjust the weights directly so that the output neurons produce the desired outputs. Experiments with remotely sensed data show the proposed algorithm consistently performs better than the conventional backpropagation learning algorithm in terms of classification accuracy and convergence speed.
We propose an algorithm for the synthesis of supervisors in discrete event systems. The algorithm is based on a learning algorithm of regular languages proposed by Angluin, and constructs a supervisor realizing an unk...
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We propose an algorithm for the synthesis of supervisors in discrete event systems. The algorithm is based on a learning algorithm of regular languages proposed by Angluin, and constructs a supervisor realizing an unknown specification which is identified through the interaction between the designer and the algorithm. We also consider the synthesis problem for systems consisting of several processes which behave concurrently. One of serious problems in dealing with such a concurrent system is that the number of states required for describing the global behavior often grows exponentially in the size of the model. To improve this situation, we introduce the concept of dependency defined on the set of events. It prevents the algorithm from considering all interleavings of independent occurrences of events.
We study solving large stochastic dynamic programming problems with simulation by using Blackwell’s approachability theorem to provide a rule of generating a (history-dependent) stochastic nonstationary policy from a...
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We study solving large stochastic dynamic programming problems with simulation by using Blackwell’s approachability theorem to provide a rule of generating a (history-dependent) stochastic nonstationary policy from a given finite set of policies whose performance is asymptotically not worse than any policy in the set by a given error. We provide an analysis for almost sure convergence with an exponentially fast convergence rate.
An intelligent condition monitoring model forpropellers and rudder of autonomous underwater vehicles(AUVs)was proposed,which was based on the FALCON with a3-step learning algorithm,The steps of the algorithm includedI...
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An intelligent condition monitoring model forpropellers and rudder of autonomous underwater vehicles(AUVs)was proposed,which was based on the FALCON with a3-step learning algorithm,The steps of the algorithm includedInltialization with fuzzy C clustering rules extraction withmaximum weights matrix and parameters fine-tuning with *** constructed the configuration of the model,analyzed theprocess of the monitoring,and discussed the method ofevaluation for the *** results of the computer simulationby actual experiment data of a certain AUV shows that thecondition monitoring model proposed In this article is feasibleand prove that the learning algorithm for the FALCON iseffective.
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