In this paper, a novel self-organizing fuzzy neural network with an adaptive learning algorithm (SOFNN-ALA) for nonlinear system modeling and identification in industrial processes is proposed. To efficiently enhance ...
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In this paper, a novel self-organizing fuzzy neural network with an adaptive learning algorithm (SOFNN-ALA) for nonlinear system modeling and identification in industrial processes is proposed. To efficiently enhance the generalization capability, the proposed SOFNN-ALA is designed by using both structure identification and parameter estimation simultaneously in the learning process. In the structure identification phase, the rule neuron with the highest neuronal activity will be split into two new rule neurons. Meanwhile, the redundant rule neurons with small singular values will be removed to simplify the network structure. In the parameter estimation phase, an adaptive learning algorithm (ALA), which is designed based on the widely used Levenberg-Marquardt (LM) optimization algorithm, is adopted to optimize the network parameters. The ALA-based learningalgorithm can not only speed up the convergence speed but also enhance the modeling performance. Moreover, we carefully analyze the convergence of the proposed SOFNN-ALA to guarantee its successful practical application. Finally, the effectiveness and efficiency of the proposed SOFNN-ALA is validated by several examples. The experimental results demonstrate that the proposed SOFNN-ALA exhibits a better comprehensive performance than some other state-of-the-art SOFNNs for nonlinear system modeling in industrial applications. The source code can be downloaded from https://***/hyitzhb/***.
Lane changing maneuver is one of the most important driving behaviors. Unreasonable lane changes can cause serious collisions and consequent traffic delays. High precision prediction of lane changing intent is helpful...
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Lane changing maneuver is one of the most important driving behaviors. Unreasonable lane changes can cause serious collisions and consequent traffic delays. High precision prediction of lane changing intent is helpful for improving driving safety. In this study, by fusing information from vehicle sensors, a lane changing predictor based on adaptive Fuzzy Neural Network (AFFN) is proposed to predict steering angles. The prediction model includes two parts: fuzzy neural network based on Takagi-Sugeno fuzzy inference, in which an improved Least Squares Estimator (LSE) is adopt to optimize parameters;adaptive learning algorithm to update membership functions and rule base. Experiments are conducted in the driving simulator under scenarios with different speed levels of lead vehicle: 60 km/h, 80 km/h and 100 km/h. Prediction results show that the proposed method is able to accurately follow steering angle patterns. Furthermore, comparison of prediction performance with several machine learning methods further verifies the learning ability of the AFNN. Finally, a sensibility analysis indicates heading angles and acceleration of vehicle are also important factors for predicting lane changing behavior. (C) 2017 Elsevier Ltd. All rights reserved.
Segmentation of pests from crop leaves is the prerequisite step for pest intelligent diagnosis. To improve the accuracy and stability of segmentation, a cognitive segmentation approach to pest images is present in thi...
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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...
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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.
A special neural networks that contain autoassociative memory (AM) - BSB- and GBSB-models are investigated at this paper. These models are implemented on hypercube and solve the task of dataset clusterization due to t...
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A special neural networks that contain autoassociative memory (AM) - BSB- and GBSB-models are investigated at this paper. These models are implemented on hypercube and solve the task of dataset clusterization due to the fact of point attraction properties of hypercube peaks. A BSB-neuro-fuzzy model can be based on BSB-model as well due to the introduction of the special fuzzy membership function. A training algorithm for the BSB- neuro-fuzzy model is proposed. This algorithm enables to enrich the BSB-neuro-fuzzy model by adaptive properties. An experiment based on medical datasets proved a high quality of the proposed model.
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...
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ISBN:
(纸本)9781467397490
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.
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. ...
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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.
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...
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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.
A chaotic neural network model with logistic mapping is proposed to improve the performance of the conventional diagonal recurrent neural network. The network shows rich dynamic behaviors that contribute to escaping f...
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A chaotic neural network model with logistic mapping is proposed to improve the performance of the conventional diagonal recurrent neural network. The network shows rich dynamic behaviors that contribute to escaping from a local minimum to reach the global minimum easily. Then, a simple parameter modulated chaos controller is adopted to enhance convergence speed of the network. Furthermore, an adaptive learning algorithm with the robust adaptive dead zone vector is designed to improve the generalization performance of the network, and weights convergence for the network with the adaptive dead zone vectors is proved in the sense of Lyapunov functions. Finally, the numerical simulation is carried out to demonstrate the correctness of the theory. (C) 2015 Elsevier B.V. All rights reserved.
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.
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