This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, ...This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.展开更多
Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and loc...Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and local least often make it difficult for the non experts to use it widely, and an improved BP (IBP) algorithm is therefore suggested to expedite the convergence speed. The algorithm can judge local least and take some steps automatically to jump out from the local least. Furthermore, this algorithm introduces the expert knowledge base. An IBP based agile and current neural network (NN) constructed tool is designed. An initial NN can be constructed automatically using an expert knowledge base. And an Aitken’s Δ 2 process method is used to expedite the convergent speed for NN. Besides, the method of changing the parameter of Sigmoid function and increasing the hidden node is used to bring surge for NN to jump out from the local展开更多
The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was prop...The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space （L＊a＊b＊, hue saturation lightness （HSL）, hue saturation value （HSV）） to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample Sr was classified by the k-NN algorithm with training set T~ according to the feature vector, which was formed from number ofpixels, eccentricity ratio, compact- ness ratio, and Euler＇s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample ofpre-training set Tz＇. The training set Tz increased to Tz＋1 by Tz＇ if Tz＇ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples （a total of 21600 frames）, the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from To to T5 by itself. Keywords multi-color space, k-nearest neighbor algorithm （k-NN）, self-learning, surge test展开更多
The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fue...The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.展开更多
文摘This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.
文摘Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and local least often make it difficult for the non experts to use it widely, and an improved BP (IBP) algorithm is therefore suggested to expedite the convergence speed. The algorithm can judge local least and take some steps automatically to jump out from the local least. Furthermore, this algorithm introduces the expert knowledge base. An IBP based agile and current neural network (NN) constructed tool is designed. An initial NN can be constructed automatically using an expert knowledge base. And an Aitken’s Δ 2 process method is used to expedite the convergent speed for NN. Besides, the method of changing the parameter of Sigmoid function and increasing the hidden node is used to bring surge for NN to jump out from the local
文摘The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space （L＊a＊b＊, hue saturation lightness （HSL）, hue saturation value （HSV）） to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample Sr was classified by the k-NN algorithm with training set T~ according to the feature vector, which was formed from number ofpixels, eccentricity ratio, compact- ness ratio, and Euler＇s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample ofpre-training set Tz＇. The training set Tz increased to Tz＋1 by Tz＇ if Tz＇ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples （a total of 21600 frames）, the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from To to T5 by itself. Keywords multi-color space, k-nearest neighbor algorithm （k-NN）, self-learning, surge test
文摘The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.
