In this paper, we introduced new adaptive learning algorithms to extract linear discriminant analysis (LDA) features from multidimensional data in order to reduce the data dimension space. For this purpose, new adapti...
详细信息
ISBN:
(纸本)9783540742586
In this paper, we introduced new adaptive learning algorithms to extract linear discriminant analysis (LDA) features from multidimensional data in order to reduce the data dimension space. For this purpose, new adaptivealgorithms for the computation of the square root of the inverse covariance matrix Sigma (-1/2) are introduced. The proof for the convergence of the new adaptivealgorithm is given by presenting the related cost function and discussing about its initial conditions. The new adaptivealgorithms are used before an adaptive principal component analysis algorithm in order to construct an adaptive multivariate multi-class LDA algorithm. adaptive nature of the new optimal feature extraction method makes it appropriate for on-line pattern recognition applications. Both adaptivealgorithms in the proposed structure are trained simultaneously, using a stream of input data. Experimental results using synthetic and real multi-class multi-dimensional sequence of data, demonstrated the effectiveness of the new adaptive feature extraction algorithm.
The Proportional Integral Derivative (PID) controller is widely used in the industrial control application, which is only suitable for the single input/single output (SISO) with known-parameters of the linear system. ...
详细信息
ISBN:
(纸本)9781467377829
The Proportional Integral Derivative (PID) controller is widely used in the industrial control application, which is only suitable for the single input/single output (SISO) with known-parameters of the linear system. However, many researchers have been proposed the neural network controller based on PID (NNPID) to apply for both of the single and multivariable control system but the NNPID controller that uses the conventional gradient descent-learningalgorithm has many disadvantages such as a low speed of the convergent stability, difficult to set initial values, especially, restriction of the degree of system complexity. Therefore, this paper presents an improvement of recurrent neural network controller based on PID, includeing a controller structure improvement and a modified extended Kalman filter (EKF) learningalgorithm for weight update rule, called ENNPID controller. We apply the proposed controller to the dynamic system including inverted pendulum, and DC motor system by the MATLAB simulation. From our experimental results, it shows that the performance of the proposed controller is higher than the other PID-like controllers in terms of fast convergence and fault tolerance that are highly required.
Impedance control is commonly used to garantee the effciency and safety of physical human-robot interaction. Classic impedance control is not able to adaptivly modify impedance parameters according to dynamic envirome...
详细信息
ISBN:
(纸本)9798350364200;9798350364194
Impedance control is commonly used to garantee the effciency and safety of physical human-robot interaction. Classic impedance control is not able to adaptivly modify impedance parameters according to dynamic enviroment. This paper used adaptive learning algorithm to modify parameters according human and enviroment variation. Model of robot and impedance control are established and corresponding experiments are conducted to verify the performancec of proposed approach. The simulation and experiment results indicated that the proposed approach can achieve a better convergence speed and robustness.
Convolution Neural Network among most of the methods for recognition has a more desirable recognition *** work introduces an adaptive learning algorithm with an adaptivelearning rate,and the algorithm is applied into...
详细信息
ISBN:
(纸本)9781509009107
Convolution Neural Network among most of the methods for recognition has a more desirable recognition *** work introduces an adaptive learning algorithm with an adaptivelearning rate,and the algorithm is applied into the human face *** solves the problems in choosing the appropriate rate and improves the slow process when faced with big *** algorithm is tested in the FERET datasets,and is compared with the traditional deep convolution neural *** test results show that this algorithm do increase the speed of convergence and reduce the recognition errors.
Electroencephalogram (EEG), also referred to as brain wave (BW), is a physiological phenomenon that depicts how the human brain functions. Brain wave analysis is fundamental in applications like brain-computer interfe...
详细信息
The power amplifier is an important device in the communication system, and its non-linear characteristics will lead to serious distortion of the output signal, reducing the performance of the communication system. In...
详细信息
The power amplifier is an important device in the communication system, and its non-linear characteristics will lead to serious distortion of the output signal, reducing the performance of the communication system. In order to solve the problem of non-linearity in a power amplifier, this study proposes an adaptive learning algorithm based on a digital pre-distortion structure, which combines direct learning and indirect learning structure. It improves the accuracy of pre-distorter parameter and has the faster convergence speed. The pre-distortion device parameters are constantly modified to achieve a good linear effect by using the recursive least square adaptivealgorithm. The simulation results show that this structure effectively compensates the output signal distortion of the power amplifier, improves the linearisation degree of the power amplifier and reduces the in-band distortion and adjacent channel leakage ratio.
This paper presents a detailed study to demonstrate the online tuning dynamic neural network PID controller to improve a joint angle position output performance of 4-joint robotic arm. The proposed controller uses a n...
详细信息
ISBN:
(纸本)9781467397506
This paper presents a detailed study to demonstrate the online tuning dynamic neural network PID controller to improve a joint angle position output performance of 4-joint robotic arm. The proposed controller uses a new updating weight rule model of the neural network architecture using multi-loop calculation of the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, called a Hybrid CKF-NNIPD controller. To evaluate the proposed controller performances, the demonstration by the Matlab simulation program is used to implement the proposed controller that connects to the 4-joint robotic arm system. In the experimental result, it shows that the proposed controller is a superior control method comparing with the other prior controllers even though the system is under the loading criteria, the proposed controller still potentially tracks the error and gives the best performances.
暂无评论