Cerebellar model articulation controller (CMAC) was developed two decades ago, yet lacks an adequate learningalgorithm. Examining the performance of a CMAC based controller showed that the control system become unsta...
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Cerebellar model articulation controller (CMAC) was developed two decades ago, yet lacks an adequate learningalgorithm. Examining the performance of a CMAC based controller showed that the control system become unstable after a long period of real time runs. A new adaptive learning algorithm is proposed. The resultant controller is applied for the trajectory tracking control of a piezoelectric actuated tool post. The performance of the proposed controller is compared with those of conventional controllers (PI controller and the conventional CMAC based controller). The experimental results showed that performance of the CMAC based controller using the proposed learningalgorithm is stable and more effective than that of the conventional controllers. (C) 2002 Elsevier Science Ltd. All rights reserved.
This article addresses the problem of interval pricing for auction items by constructing an auction item price prediction model based on an adaptive learning algorithm. Firstly, considering the confusing class charact...
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This article addresses the problem of interval pricing for auction items by constructing an auction item price prediction model based on an adaptive learning algorithm. Firstly, considering the confusing class characteristics of auction item prices, a dynamic interclass distance adaptivelearning model is developed to identify confusing classes by calculating the differences in prediction values across multiple classifiers for target domain samples. The difference in the predicted values of the target domain samples on multiple classifiers is used to calculate the classification distance, distinguish the confusing classes, and make the similar samples in the target domain more clustered. Secondly, a deep clustering algorithm is constructed, which integrates the temporal characteristics and numerical differences of auction item prices, using DTW-K-medoids based dynamic time warping (DTW) and fuzzy C-means (FCM) algorithms for fine clustering. Finally, the KF-LSTM auction item interval price prediction model is constructed using long short-term memory (LSTM) and dual clustering. Experimental results show that the proposed KF-LSTM model significantly improves the prediction accuracy of auction item prices during fluctuation periods, with an average accuracy rate of 90.23% and an average MAPE of only 5.41%. Additionally, under confidence levels of 80%, 85%, and 90%, the KF-LSTM model achieves an interval coverage rate of over 85% for actual auction item prices, significantly enhancing the accuracy of auction item price predictions. This experiment demonstrates the stability and accuracy of the proposed model when applied to different sets of auction items, providing a valuable reference for research in the auction item price prediction field.
In this article toe propose the adaptive learning algorithm, of neural network with respect to a rapid temperature change of forecasted day. The proposed adaptive learning algorithm is used to shift the learning range...
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In this article toe propose the adaptive learning algorithm, of neural network with respect to a rapid temperature change of forecasted day. The proposed adaptive learning algorithm is used to shift the learning range of previous year of forecasted day. Therefore, the proposed neural network can be trained by using learning data, including the maximum temperature to be forecasted. The suitability of the proposed approach is illustrated through an application to actual load data of Okinawa Electric Power Company in Japan.
An artificial neural network with an adaptive-Kalman-filter-based learningalgorithm is presented for forecasting weather-sensitive loads. The proposed model can differentiate between weekday and weekend loads, This n...
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An artificial neural network with an adaptive-Kalman-filter-based learningalgorithm is presented for forecasting weather-sensitive loads. The proposed model can differentiate between weekday and weekend loads, This neural-network model has been implemented using real load data, The results reveal the efficiency and accuracy of the proposed approach in terms of short learning time, rapid convergence and the adaptive nature of the learningalgorithm.
This article presents the development and implementation of an artificial neural network (ANN) for controlling a new two-degree-of-freedom (2DOF) serial ball-and-socket actuator. The ANN is a well-known algorithm for ...
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This article presents the development and implementation of an artificial neural network (ANN) for controlling a new two-degree-of-freedom (2DOF) serial ball-and-socket actuator. The ANN is a well-known algorithm for simulating the ability of the human brain to learn and predict sets of information. In this approach, ANN will learn the control parameters to obtain the angular displacement, angular velocity, and angular acceleration of the end-effector without any prior knowledge of the actuator. The ball-and-socket actuator has been proposed as an alternative actuator to the conventional one-degree-of-freedom (1DOF) revolute actuator. The actuator was fabricated from a ball-and-socket joint powered by two electrohydraulic cylinders. Experimental control data had been collected manually and provided for ANN to learn in off-line mode. The training process was carried out to build control knowledge. Thus, the adaptive learning algorithm adopts any modification in the actuator mechanism and hydraulic power system through updating the control knowledge. The results of implementing the build control knowledge for on-line operation of the ball-and-socket actuator shows a fully compliant actuator end-effector to the desired dynamic behaviour within the workspace.
Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learningalgorithm, especially when it is used in a hybrid-type...
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Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learningalgorithm, especially when it is used in a hybrid-type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learningalgorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid-type controller is proposed. The main features of the proposed learningalgorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learningalgorithm is a robust in stabilizing the control system. Also, this proposed learningalgorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learningalgorithm experimentally.
Aiming at the problem that the inference rules are large and the parameters are difficult to identify in multidimensional fuzzy reasoning, a TS-type fuzzy RBF neural network model is constructed based on the TS-type f...
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ISBN:
(纸本)9781538681787
Aiming at the problem that the inference rules are large and the parameters are difficult to identify in multidimensional fuzzy reasoning, a TS-type fuzzy RBF neural network model is constructed based on the TS-type fuzzy inference system and the RBF neural network function equivalence principle, and a TS fuzzy model based on TS fuzzy model is proposed The adaptive learning algorithm of RBF neural network can not only dynamically adjust the number of hidden nodes in TS-type fuzzy RBF network, but also adaptively change the data center value of the network. Finally, the effectiveness of the algorithm is verified by MATLAB software simulation. The feasibility shows that the TS-type fuzzy RBF network can quickly approximate any multivariable nonlinear function and has better adaptive ability and generalization ability.
The research topic of this article is the English personalized learning recommendation module based on Markov chain algorithm and adaptive learning algorithm. Personalized learning recommendation systems have been wid...
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The research topic of this article is the English personalized learning recommendation module based on Markov chain algorithm and adaptive learning algorithm. Personalized learning recommendation systems have been widely applied in the field of education. Firstly, this article uses Markov chain algorithm to analyze user learning behavior data, abstract user learning behavior as a process of state transition, predict their future behavior based on their past behavior, better understand their learning needs and interests, and provide more personalized learning recommendations for them. Subsequently, combined with adaptive learning algorithms, recommendation strategies and content are dynamically adjusted based on the user's learning goals and level, providing more accurate and effective learning recommendations according to their personalized needs and learning progress. By comparing with traditional recommendation systems, evaluate the accuracy and personalization of the recommendation module to better meet the learning needs of users.
The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy pr...
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The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.
In this paper, adaptive learning algorithms to obtain better generalization performance are proposed. We specifically designed cost terms for the additional functionality based on the first- and second-order derivativ...
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In this paper, adaptive learning algorithms to obtain better generalization performance are proposed. We specifically designed cost terms for the additional functionality based on the first- and second-order derivatives of neural activation at hidden layers. In the course of training, these additional cost functions penalize the input-to-output mapping sensitivity and high-frequency components in training data. A gradient-descent method results in hybrid learning rules to combine the error back-propagation, Hebbian rules, and the simple weight decay rules. However, additional computational requirements to the standard error back-propagation algorithm are almost negligible. Theoretical justifications and simulation results are given to verify the effectiveness of the proposed learningalgorithms. (C) 2000 Elsevier Science B.V. All rights reserved.
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