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In this paper, we have proposed a new architecture of RBFNN. Neural network efficiency in embedded systems offers the possibility of reconfiguration and the genericity of the solution. Indeed, the same integrated system can approximate any input-output function thanks to the parameters update on the chip. RBF neural networks constitute a subset of the neuronal networks, which has a great potential...
This paper discusses a method for chaotic time series prediction based on radial basis function (RBF) neural network. The number of input nodes for RBF is determined by embedding dimension based on chaotic phase-space reconstruction. Both Grassberger--Procaccia algorithm and Takens' method are employed to calculate minimal embedding dimension of chaotic time series. Finally, the prediction accuracy...
A new disaster monitor and forecast system based on RBF neural networks is proposed. This disaster forecast system consists of disaster spatial monitor subsystem that is pre-trained by off-line learning algorithms and disaster time forecast subsystem developed by online learning algorithms. The disaster spatial monitor subsystem aims to detect trend of the objective behavior, once the unstable condition...
A parallel chaos optimization algorithm based on probability selection is proposed to resolve the function optimization problems. The searching space divides into origin space and elaborate space. During the optimization, the two spaces are searched synchronously according to different probability. The boundary of the elaborate space is decreased continuously, and its searching probability is increased...
Brushless DC motor speed servo system is multivariable, nonlinear and strong coupling. Its performance is easily influenced by the parameter variation, the cogging torque and the load disturbance. To solve the deficiency, the paper represents the algorithm of active-disturbance rejection control (ADRC) based on back-propagation (BP) neural network. The ADRC is independent of accurate system and its...
This paper describes a position control of 2 degrees of freedom (DOF) XY Piezo Actuator Stage (XY PAS) with Feedforward Neural Network (FNN) and additional Particle Swarm Optimization (PSO) approach, which is used as an improved learning method for optimizing the weights of FNN rather than just the standard technique of back-propagation of errors.
In order to get rid of the limit of traditional methods and provide a decision making reference for the supervision of securities organizations and the risk control of investors, A novel model based on SOM2W network (SOM with 2 winners self-organizing map) is proposed for assessment financial performance of the listed companies. In addition, a tabu-mapping method is proposed to avoid that the same...
The end effects of Hilbert-Huang transform are produced in the Empirical Mode Decomposition(EMD) and the Hilbert transform for Intrinsic Mode Functions(IMF), which have a badly effect on Hilbert-Huang transform. In order to overcome this problem, the multi-objective allocation Genetic Algorithm (GA) to solve the kernel parameters selection of Least Squares Support Vector Machine (LSSVM)(GLHHT) is...
This paper is to present a novel design of a multi-functional controller for the electronic belt scale. The data acquisition unit in this controller is separated from the main control unit. As the control unit is located at a long distant from the electronic belt scale, data communication using RS-485 avoid error caused by EMI. The data acquisition unit and control unit are portable and are easy to...
This paper forwards a neural network based VLSI power estimation on VLSI chip specification. This paper used neural network to perform VLSI power estimation. Experiments were made on chip specification parameters extracted from the datasheet of TI series micro-controllers. Different net structure, training plans and vector organizations were applied. Based on limited number of test vector, experimental...
It is well known that lattice filters have excellent finite word length properties and that there are five elementary lattice building blocks. A lattice structure may yield very different figures of performance when different elementary lattice blocks are used. This allows us to optimize the structure w.r.t. these elementary blocks. In this paper, a new lattice configuration is proposed, in which...
Due to simplicity and low cost, induction motors are more useful than direct current motors. Hence the control of these motors is important. The pervious methods are fitted normally for a limited speed range and could not be used for both low speed and high speed. The voltage model is suitable for high speed because the voltage drop of stator resistance is not small in low speed voltage. The current...
This paper introduces a new design for the Bulk Built-In Current Sensor (BICS) capable of detecting the single-event transients (SET) due to a particle strike in the integrated circuits. An 4-bit multiplier is used as a case study. The simulation results indicate that efficiency and applicability of the Bulk-BICS of this work improves while the power consumption and area overhead reduce greatly.
In this paper, the adaptive Kaman linear filtering simulation results show that there are many differences between the two different maneuvering acceleration distributing of current statistical model, when the acceleration is changing while the tracking of maneuvering targets. At the end, the reason why so many differences between the two different maneuvering acceleration distributing is given.
An adaptive speed PID controller based on RBFNN is investigated for PMSM servo system in this paper. RBF is used to identify the plant first. Then the Jacobian information of controlled plant obtained by RBFNN identification is used to adjust the gain of speed PID controller on-time. A robust parameters self-tuning learning algorithm is adopted to train the ANN for fast convergence. Simulation result...
In order to accelerate the learning speed of the conventional CMAC, an Improved Credit Assigned CMAC (ICA-CMAC) is presented in this paper. And then the proposed ICA-CMAC is applied to approaching two objective functions. Simulation results show that the ICA-CMAC has faster learning speed. In addition, the paper discussed different performances of ICA-CMAC influenced by different learning rates. It...
Based on the study of traditional Simple adaptive control (SAC), the dissertation combines SAC with intelligence control algorithms, and proposes a new Intelligent SAC. The basic algorithm of SAC is improved, and cerebellar model articulation controller algorithm of neural network is combined with the SAC algorithm, the SAC controller adopts the control algorithm of adaptive method, but the construction...
A multi-layer forward neural network acted as the inverse controller, which was trained with predictive optimization algorithm to compensate for disturbances and uncertain plant nonlinearities, and reverse control based on neural network is implemented in complicated non-linear system. The weights of neural network inverse control were trained by multi-step predictive index function, thereby the system...
This paper for the shortcomings of conventional BP algorithm which has slow convergence and falls into local minimum easily, the nonlinear self-feedback term is introduced into this algorithm. Thus chaotic BP algorithm (CBPA) is given. The weight of fuzzy neural network (FNN) is trained and learned by using it. Thus an introduction-type fuzzy chaotic neural network (IFCNN) is constituted. Then simulation...
With the combination of T-S fuzzy model and conventional PID controller, as well as by means of the self-learning and self-organizing ability of neural network, an intelligent control algorithm based on T-S fuzzy neural network PID control is presented in this paper. With this control algorithm, an pressure controller for solenoid valve test was designed and the corresponding simulation was carried...
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