This paper presents an implementation of a pulse mode multilayer neural network with on chip learning. Taking advantage of the compactness of the multiplierless solutions proposed in the literature, we apply a multipl...
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This paper presents an implementation of a pulse mode multilayer neural network with on chip learning. Taking advantage of the compactness of the multiplierless solutions proposed in the literature, we apply a multiplierless architecture, in which the synapse is made up with a DDFS and the neuron uses a nonlinear adder. A programmable activation function is proposed by means of an adjustable pulse multiplier so that the activation function slope can be adjusted without any added hardware cost. The proposed architecture was tested in a signature recognition system. It shows good learning capability. The corresponding design was implemented into a Virtex II PRO XC2VP7 Xilinx FPGA
This paper presents an implementation of a signature recognition system based on pulse mode multilayer neural networks with on chip learning. Taking advantage of the compactness of the multiplierless solutions of puls...
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This paper presents an implementation of a signature recognition system based on pulse mode multilayer neural networks with on chip learning. Taking advantage of the compactness of the multiplierless solutions of pulse mode operations, we apply an architecture, in which the synapse is made up with a DDFS and the neuron uses a nonlinear adder. A programmable activation function is proposed by means of an adjustable pulse multiplier so that the activation function slope can be adjusted without any added hardware cost. Good learning capability is obtained. As illustration, we consider a signature learning application. The corresponding design was implemented into an FPGA platform ( virtex II PRO XC2VP7).
In this paper, a low voltage current conveyor (CCII) based multifunction filter is presented. Firstly, thanks to an optimizing heuristic, an optimal sizing of a low voltage low power CMOS current conveyor (CCII) was d...
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In this paper, a low voltage current conveyor (CCII) based multifunction filter is presented. Firstly, thanks to an optimizing heuristic, an optimal sizing of a low voltage low power CMOS current conveyor (CCII) was done. Hence, we improve static and dynamic performances of this configuration. The optimized CCII configuration has a current bandwidth of 1.103GHz and a voltage bandwidth of 1.18GHz and 33.4Ω as RX parasitic resistance value. Secondly, implementation of a multifunction filter based on this configuration was done. The current mode filter has a tunable central frequency in the range [50MHz-800MHz]. PSPICE simulations are presented to demonstrate these results.
This article presents an adaptive multilayer neural network-based controller that feedback-linearizes the system for a class of single-input single-output (SISO) and multi-input multi-output (MIMO) continuous-time non...
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This article presents an adaptive multilayer neural network-based controller that feedback-linearizes the system for a class of single-input single-output (SISO) and multi-input multi-output (MIMO) continuous-time nonlinear systems. control action is used to achieve tracking performances for state-feedback linearizable unknown non-linear system. The control structure consists of a feedback lineariza- tion portion provided by neural networks (NN). In the standard problem of feedback-based control, the cost to minimize is a func- tion of the output derivatives. When the cost function depends on the output error, the gradient method cannot be applied to adjust the neural network parameters. In this context, the stochastic approximation approach allows computation of the cost function derivatives. In order to show the feasibility and performance of this control scheme, two applications are chosen as nonlinear case studies.
In this papery we are concerned with face recognition techniques using principal component analysis (PCA), Fisher linear discriminant Analysis (FLDA) as linear approaches and Neural Networks. Known as one of the effic...
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This work provides algorithms for underactuated ship path guidance using nonlinear control theory. Path following is achieved by a geometric assignment based on a line-of-sight projection algorithm for minimization of...
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ISBN:
(纸本)907738121X
This work provides algorithms for underactuated ship path guidance using nonlinear control theory. Path following is achieved by a geometric assignment based on a line-of-sight projection algorithm for minimization of the cross-track error to the path. The control laws in surge and yaw are derived using backstepping. This results in a dynamic feedback controller where the dynamics of the uncontrolled sway mode enters the yaw control law. Uniform Global Asymptotic Stability (UGAS) is proven for the tracking error dynamics in surge and yaw while the controller dynamics is bounded. To show the effectiveness of the controller and guidance systems, we simulate our algorithm using Simulink on a marine craft model.
Texture segmentation via wavelet transform traditionally adopts textural features based approach. However, applying this method can lead to over-segmentation problems. To overcome this limitation, we propose a new sch...
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This research is aimed to the development of a dynamic control to enhance the performance of the existing dynamic controllers for mobile robots. System dynamics of the car-like robot with nonholonomic constraints were...
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This research is aimed to the development of a dynamic control to enhance the performance of the existing dynamic controllers for mobile robots. System dynamics of the car-like robot with nonholonomic constraints were employed. A Backstepping approach for the design of discontinuous state feedback controller is used for the design of the controller. It is shown that the origin of the closed loop system can be made stable in the sense of Lyapunov. The control design is made on the basis of a suitable Lyapunov function candidate. The effectiveness of the proposed approach is tested through simulation on a car-like vehicle mobile robot.
The aim of this paper considers the determination of optimal control trajectories of a complex process. The proposed method is based on the decomposition of the system into interconnected subsystems. We consider the c...
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The aim of this paper considers the determination of optimal control trajectories of a complex process. The proposed method is based on the decomposition of the system into interconnected subsystems. We consider the cases where subsystems are linear in terms of their state and control vectors. For this reason, a neural network is identified which compute local gains. Genetic algorithms are used to optimize the networks weights. Simulation results show that the proposed approximations yield satisfactory performances.
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