Stroke is a major cause of disability in worldwide and also one of the causes of death after coronary heart disease. Many devices had been designed for hand motor function rehabilitation that a stroke survivor can use...
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Stroke is a major cause of disability in worldwide and also one of the causes of death after coronary heart disease. Many devices had been designed for hand motor function rehabilitation that a stroke survivor can use for bilateral movement practice. This paper presents an arm motor function rehabilitation device where it is designed to predict the position angle for the robotic arm. MATLAB software is used for real-time positioning that can be developed by SIMULINK block diagram and proof by the simulator in program code in order for devising to operate under the position demand. All the angular motions or feedback to the simulation mode from the attached optical encoders via the Data Acquisition Card (DAQ). The learning algorithm can directly determine the position of its joint and can therefore completely eliminate the need for any system modelling. The robotic arm shows a successful implementation of the learning algorithm in predicting the behavior for arm exoskeleton.
A parallel multi-layer perceptron network (MLPN) model with on-line learning algorithm is proposed. This parallel MLPN is on-line trained directly in a parallel form. The on-line learning algorithm is based on the Ext...
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A parallel multi-layer perceptron network (MLPN) model with on-line learning algorithm is proposed. This parallel MLPN is on-line trained directly in a parallel form. The on-line learning algorithm is based on the Extended Kalman Filter (EKF) algorithm. This parallel MLPN is able to learn the non-linear dynamic behaviour of unknown time-varying systems. The proposed parallel MLPN can be used to model the non-linear systems and perform multi-step-ahead prediction for control purpose. The performance of this model is demonstrated in modelling a multi-variable nonlinear continuous stirred tank reactor (CSTR).
In the manufacturing industry, production efficiency is the core competitiveness of enterprises, but also an important goal of enterprise production planning management, and in recent years, the production efficiency ...
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In the manufacturing industry, production efficiency is the core competitiveness of enterprises, but also an important goal of enterprise production planning management, and in recent years, the production efficiency of China's manufacturing industry does not meet the requirements, and there are still many problems to be solved. In the face of this situation, the relevant fields have explored intelligent manufacturing and proposed intelligent manufacturing technology, which mainly integrates information technology with traditional manufacturing system, can realize the important measures of intelligence, automation, digitalization and information in the manufacturing process, and can help solve the current key problems. Therefore, this paper takes ship manufacturing as the background. The real-time optimization technology of intelligent ship manufacturing based on learning algorithm is studied. This technology can effectively improve ship production efficiency and reduce production cost.
In this paper, a learning algorithm considering derivative information is proposed for neural networks. Based on backpropagation (BP) algorithm, this algorithm takes the derivative information of the samples into acco...
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This paper proposes a fast learning algorithm of neural networks and evaluates the performances of adaptive equalizers using neural networks trained by the proposed algorithm in a frequency-selective fading channel. T...
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This paper proposes a fast learning algorithm of neural networks and evaluates the performances of adaptive equalizers using neural networks trained by the proposed algorithm in a frequency-selective fading channel. The backpropagation (BP) algorithm which is used widely to train neural networks has a slow convergence rate because it is based on the gradient descent method. This paper presents a fast learning algorithm using the recursive least squares (RLS) algorithm which has a fast convergence rate as an adaptive algorithm for adaptive linear filters. In the proposed algorithm, the sum of the squared error between the actual total input and the desired total input is used as the cost function to apply the RLS algorithm. A simulation result on the exclusive-OR problem indicates that the proposed algorithm is about 8.8 times faster than the BP for the number of iterations required to converge. Recently, there has been interest in adaptive equalizers as an application field of neural networks. However, the performance of an adaptive equalizer using a neural network in a frequency-selective fading channel which is observed in land mobile communications has never been evaluated. Therefore, in this paper, the performances of adaptive equalizers using neural networks trained by the proposed algorithm in a frequency-selective fading channel are evaluated. Especially, an adaptive equalizer using the selectively unsupervised learning neural network proposed by the authors is considered. The adaptive equalizer can reject the false learning by carrying out learning selectively. It is shown that the adaptive equalizer is superior to the conventional one and the one using the conventional neural network.
Metasearching is the process of combining search results of different search systems into a single set of ranked results, which is expected to be better than results of best of the participating search systems. In thi...
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ISBN:
(纸本)1424415500
Metasearching is the process of combining search results of different search systems into a single set of ranked results, which is expected to be better than results of best of the participating search systems. In this paper, we present a supervised learning algorithm for metasearching. Our algorithm learns the ranking rules on the basis of user feedback based metasearching for the queries in the training set. We use rough set theory to mine the ranking rules. The ranking rules are validated using cross validation. The best of the ranking rules is then used to estimate the results of metasearching for the other queries. We compare our method with modified Shimura technique. We claim that our method is more useful than modified Shimura technique as it models userpsilas preference.
SVM is the structural risk minimization of statistical learning theory developed on the basis of a pattern recognition method, based on limited sample information and the complexity of the model to find the best compr...
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SVM is the structural risk minimization of statistical learning theory developed on the basis of a pattern recognition method, based on limited sample information and the complexity of the model to find the best compromise between the generalization ability. As there is a supervised learning method, the standard SVM classification requires supervised learning algorithm based on the principle: from a limited number of labeled samples to learn the rules and the rule extended to the unknown non-tag samples.
In this paper, a novel algorithm named DNA-like learning algorithm is proposed. This algorithm is enable to quickly train the CNN template implementing LSBF, and has many advantages including without the need to consi...
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In this paper, a novel algorithm named DNA-like learning algorithm is proposed. This algorithm is enable to quickly train the CNN template implementing LSBF, and has many advantages including without the need to consider its convergence property, in particular faster running speed and better robustness. The important problem of implementing non-LSBF will be further discussed in the future study.
The strategy using approximate/adaptive dynamic programming(ADP) has been widely used to design a learning controller for complex systems of higher dimension in recent *** paper aims at handling an important problem i...
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The strategy using approximate/adaptive dynamic programming(ADP) has been widely used to design a learning controller for complex systems of higher dimension in recent *** paper aims at handling an important problem in the design of ADP learning controllers,which is the improvement of learning algorithm for its convergence *** analyze ADP controller implementation framework according to the requirement of tracking control task,with emphasis on providing an improved weight-updating gradient descent approach in optimizing connection weights in network structures.A comparison of the proposed method and classic ADP design for tracking and controlling pitch angle of aircraft is *** verifies the feasibility in the design of the proposed ADP based controller.
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