The Experts below are selected from a list of 974412 Experts worldwide ranked by ideXlab platform
Yukinori Kakazu - One of the best experts on this subject based on the ideXlab platform.
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on line Learning Method for emg prosthetic hand control
Electronics and Communications in Japan Part Iii-fundamental Electronic Science, 2001Co-Authors: Daisuke Nishikawa, Hiroshi Yokoi, Yukinori KakazuAbstract:This paper proposes a novel Learning Method for prosthetic hand control. Conventional works have used off-line Learning Methods for control, and hence two kinds of training must be carried out separately: one is for the amputee to control prosthetic hands, and the other is for the prosthetic hand controller to adapt to the amputee's variations. Consequently, an amputee cannot acquire the sensations of operating prosthetic hands through training, and nevertheless he or she is likely to experience difficulties in forcing the prosthetic hand controller to follow the change of his or her own characteristics in practical use. We accordingly design an on-line Learning mechanism which can adapt to the individual's characteristics in real time. Using this mechanism, a suitable mapping function of the surface electromyogram (EMG) to motions of prosthetic hands can be acquired according to the amputee's evaluation in practical use. Thereby, the mechanism realizes a shortening of training time and adaptation to individual variation in real time. The experiments succeeded in discriminating six forearm motions to verify the proposed Method. First, we use intrinsic exercise images to control a prosthetic hand, and compare our on-line Method with one conventional off-line Method. Second, we use EMG signals on shoulder girdles to control the prosthetic hand for upper elbow amputation. The discrimination rate in forearm EMG experiments is 89.9% by our Method and 80.3% by the conventional Method. Moreover, we show the possibility of applying the on-line Learning Method to upper elbow amputees, because a discrimination rate of 79.3% is achieved by our Method in shoulder girdle EMG classification. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(10): 35–46, 2001
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emg prosthetic hand controller discriminating ten motions using real time Learning Method
Intelligent Robots and Systems, 1999Co-Authors: Daisuke Nishikawa, Hiroshi Yokoi, Yukinori KakazuAbstract:We discuss the necessity of a Learning mechanism for an EMG prosthetic hand controller, and the real-time Learning Method is proposed and designed. This Method divides the controller into three units. The analysis unit extracts useful informations for discriminating motions from the EMG. The adaptation unit learns the relation between EMG and control command and adapts operator's characteristics. The trainer unit makes the adaptation unit learn in real-time. Experiments show that the proposed controller discriminates ten forearm motions, which contain four wrist motions and six hand motions, and learns within 4/spl sim/25 minutes. The average of the discriminating rate is 91.5%.
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EMG prosthetic hand controller using real-time Learning Method
IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems Man and Cybernetics (Cat. No.99CH37028), 2024Co-Authors: Daisuke Nishikawa, Hiroshi Yokoi, Yukinori KakazuAbstract:This paper reports the prosthetic hand controller discriminating ten forearm motions from two channels of EMG signals. The controller uses the real-time Learning Method that is defined as simultaneously controlling a prosthetic hand and Learning to adapt to the operator's characteristics. In this Method, the controller is divided into three units. The analysis unit extracts useful information for discriminating motions from EMG. The adaptation unit learns the relation between EMG and control command and adapts to the operator's characteristics. The trainer unit generates training data and makes the adaptation unit learn in real-time. In experiments, the proposed controller performs discriminating a maximum of ten forearm motions including four wrist motions and six hand motions. In an eight forearm motions experiment, the five subjects' average discriminating rate, which serves an index of accurate controlling, was 85.1%. Two groups occur from this result, one marks a high performance (91.7%) and another does not (75.2%). The paper discusses the factors of this difference in performance from both phases of training and reasons that the low proficiency leads to undesirable results in the latter groups. Besides, in the ten forearm motions experiment the average discriminating rate of three subjects who achieve high performance in the previous experiment was 91.5%. This paper concludes that the effectiveness of the real-time Learning Method is confirmed by these experiments.
Shuiming Zhong - One of the best experts on this subject based on the ideXlab platform.
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an online supervised Learning Method based on gradient descent for spiking neurons
Neural Networks, 2017Co-Authors: Jing Yang, Shuiming ZhongAbstract:The purpose of supervised Learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) Learning Methods are widely used and verified in the current research. Although the existing GDB multi-spike Learning (or spike sequence Learning) Methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence Learning Method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The Method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our Method obviously improves Learning performance compared with the offline Learning manner and has certain advantage on Learning accuracy compared with other Learning Methods. Stronger Learning ability determines that the Method has large pattern storage capacity.
Daisuke Nishikawa - One of the best experts on this subject based on the ideXlab platform.
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on line Learning Method for emg prosthetic hand control
Electronics and Communications in Japan Part Iii-fundamental Electronic Science, 2001Co-Authors: Daisuke Nishikawa, Hiroshi Yokoi, Yukinori KakazuAbstract:This paper proposes a novel Learning Method for prosthetic hand control. Conventional works have used off-line Learning Methods for control, and hence two kinds of training must be carried out separately: one is for the amputee to control prosthetic hands, and the other is for the prosthetic hand controller to adapt to the amputee's variations. Consequently, an amputee cannot acquire the sensations of operating prosthetic hands through training, and nevertheless he or she is likely to experience difficulties in forcing the prosthetic hand controller to follow the change of his or her own characteristics in practical use. We accordingly design an on-line Learning mechanism which can adapt to the individual's characteristics in real time. Using this mechanism, a suitable mapping function of the surface electromyogram (EMG) to motions of prosthetic hands can be acquired according to the amputee's evaluation in practical use. Thereby, the mechanism realizes a shortening of training time and adaptation to individual variation in real time. The experiments succeeded in discriminating six forearm motions to verify the proposed Method. First, we use intrinsic exercise images to control a prosthetic hand, and compare our on-line Method with one conventional off-line Method. Second, we use EMG signals on shoulder girdles to control the prosthetic hand for upper elbow amputation. The discrimination rate in forearm EMG experiments is 89.9% by our Method and 80.3% by the conventional Method. Moreover, we show the possibility of applying the on-line Learning Method to upper elbow amputees, because a discrimination rate of 79.3% is achieved by our Method in shoulder girdle EMG classification. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(10): 35–46, 2001
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emg prosthetic hand controller discriminating ten motions using real time Learning Method
Intelligent Robots and Systems, 1999Co-Authors: Daisuke Nishikawa, Hiroshi Yokoi, Yukinori KakazuAbstract:We discuss the necessity of a Learning mechanism for an EMG prosthetic hand controller, and the real-time Learning Method is proposed and designed. This Method divides the controller into three units. The analysis unit extracts useful informations for discriminating motions from the EMG. The adaptation unit learns the relation between EMG and control command and adapts operator's characteristics. The trainer unit makes the adaptation unit learn in real-time. Experiments show that the proposed controller discriminates ten forearm motions, which contain four wrist motions and six hand motions, and learns within 4/spl sim/25 minutes. The average of the discriminating rate is 91.5%.
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EMG prosthetic hand controller using real-time Learning Method
IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems Man and Cybernetics (Cat. No.99CH37028), 2024Co-Authors: Daisuke Nishikawa, Hiroshi Yokoi, Yukinori KakazuAbstract:This paper reports the prosthetic hand controller discriminating ten forearm motions from two channels of EMG signals. The controller uses the real-time Learning Method that is defined as simultaneously controlling a prosthetic hand and Learning to adapt to the operator's characteristics. In this Method, the controller is divided into three units. The analysis unit extracts useful information for discriminating motions from EMG. The adaptation unit learns the relation between EMG and control command and adapts to the operator's characteristics. The trainer unit generates training data and makes the adaptation unit learn in real-time. In experiments, the proposed controller performs discriminating a maximum of ten forearm motions including four wrist motions and six hand motions. In an eight forearm motions experiment, the five subjects' average discriminating rate, which serves an index of accurate controlling, was 85.1%. Two groups occur from this result, one marks a high performance (91.7%) and another does not (75.2%). The paper discusses the factors of this difference in performance from both phases of training and reasons that the low proficiency leads to undesirable results in the latter groups. Besides, in the ten forearm motions experiment the average discriminating rate of three subjects who achieve high performance in the previous experiment was 91.5%. This paper concludes that the effectiveness of the real-time Learning Method is confirmed by these experiments.
Qinglai Wei - One of the best experts on this subject based on the ideXlab platform.
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neural network based synchronous iteration Learning Method for multi player zero sum games
Neurocomputing, 2017Co-Authors: Ruizhuo Song, Qinglai Wei, Biao SongAbstract:In this paper, a synchronous solution Method for multi-player zero-sum games without system dynamics is established based on neural network. The policy iteration (PI) algorithm is presented to solve the HamiltonJacobiBellman (HJB) equation. It is proven that the obtained iterative cost function is convergent to the optimal game value. For avoiding system dynamics, off-policy Learning Method is given to obtain the iterative cost function, controls and disturbances based on PI. Critic neural network (CNN), action neural networks (ANNs) and disturbance neural networks (DNNs) are used to approximate the cost function, controls and disturbances. The weights of neural networks compose the synchronous weight matrix, and the uniformly ultimately bounded (UUB) of the synchronous weight matrix is proven. Two examples are given to show that the effectiveness of the proposed synchronous solution Method for multi-player ZS games.
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echo state network based q Learning Method for optimal battery control of offices combined with renewable energy
Iet Control Theory and Applications, 2017Co-Authors: Guang Shi, Derong Liu, Qinglai WeiAbstract:An echo state network (ESN)-based Q-Learning Method is developed for optimal energy management of an office, where the solar energy is introduced as the renewable source, and a battery is installed with a control unit. The energy consumption in the office, also considered as the energy demand, is separated into those from sockets, lights and air-conditioners. First, ESNs, well known for their excellent modelling performance for time series, are employed to model the time series of the real-time electricity rate, renewable energy and energy demand as periodic functions. Second, given the periodic models of the electricity rate, renewable energy and energy demand, an ESN-based Q-Learning Method with the Q-function approximated by an ESN is developed and implemented to determine the optimal charging/discharging/idle strategies for the battery in the office, so that the total cost of electricity from the grid can be reduced. Finally, numerical analysis is conducted to illustrate the performance of the developed Method.
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a novel dual iterative q Learning Method for optimal battery management in smart residential environments
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Qinglai Wei, Derong Liu, Guang ShiAbstract:In this paper, a novel iterative $Q$ -Learning Method called “dual iterative $Q$ -Learning algorithm” is developed to solve the optimal battery management and control problem in smart residential environments. In the developed algorithm, two iterations are introduced, which are internal and external iterations, where internal iteration minimizes the total cost of power loads in each period, and the external iteration makes the iterative $Q$ -function converge to the optimum. Based on the dual iterative $Q$ -Learning algorithm, the convergence property of the iterative $Q$ -Learning Method for the optimal battery management and control problem is proven for the first time, which guarantees that both the iterative $Q$ -function and the iterative control law reach the optimum. Implementing the algorithm by neural networks, numerical results and comparisons are given to illustrate the performance of the developed algorithm.
Masayuki Takahashi - One of the best experts on this subject based on the ideXlab platform.
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asymptotic expansion as prior knowledge in deep Learning Method for high dimensional bsdes
Asia-pacific Financial Markets, 2019Co-Authors: Masaaki Fujii, Akihiko Takahashi, Masayuki TakahashiAbstract:We demonstrate that the use of asymptotic expansion as prior knowledge in the “deep BSDE solver”, which is a deep Learning Method for high dimensional BSDEs proposed by Weinan et al. (Deep Learning-based numerical Methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, 2017b. arXiv:1706.04702 ), drastically reduces the loss function and accelerates the speed of convergence. We illustrate the technique and its implications by using Bergman’s model with different lending and borrowing rates as a typical model for FVA as well as a class of solvable BSDEs with quadratic growth drivers. We also present an extension of the deep BSDE solver for reflected BSDEs representing American option prices.
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asymptotic expansion as prior knowledge in deep Learning Method for high dimensional bsdes
Asia-pacific Financial Markets, 2019Co-Authors: Masaaki Fujii, Akihiko Takahashi, Masayuki TakahashiAbstract:We demonstrate that the use of asymptotic expansion as prior knowledge in the "deep BSDE solver", which is a deep Learning Method for high dimensional BSDEs proposed by Weinan E, Han & Jentzen (2017), drastically reduces the loss function and accelerates the speed of convergence. We illustrate the technique and its implications by using Bergman's model with different lending and borrowing rates as a typical model for FVA as well as a class of solvable BSDEs with quadratic growth drivers. We also present an extension of the deep BSDE solver for reflected BSDEs representing American option prices.
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asymptotic expansion as prior knowledge in deep Learning Method for high dimensional bsdes
arXiv: Computational Finance, 2017Co-Authors: Masaaki Fujii, Akihiko Takahashi, Masayuki TakahashiAbstract:We demonstrate that the use of asymptotic expansion as prior knowledge in the "deep BSDE solver", which is a deep Learning Method for high dimensional BSDEs proposed by Weinan E, Han & Jentzen (2017), drastically reduces the loss function and accelerates the speed of convergence. We illustrate the technique and its implications by Bergman's model with different lending and borrowing rates, and a class of quadratic-growth BSDEs. We also present an extension of the deep BSDE solver for reflected BSDEs using an American basket option as an example.
