The ability of a neural network to realize some complex nonlinear function makes them attractive for system identification. In the recent past, neural networks trained with back-propagation (BP) learning algorithm have gained attention for the identification of nonlinear dynamic systems. Slower convergence and longer training times are the disadvantages often mentioned when the standard BP algorithm are compared with other competing techniques. In addition, in the standard BP algorithm, the learning rate is fixed and that it is uniform for all weights in a layer. In this paper, we present an improvement to the standard BP algorithm based on the use of an adaptive learning rate and momentum term, where the learning rate is adjusted at each iteration to reduce the training time. Simulation results indicate a faster convergence speed and better error minimization as compared to other competing methods.
This paper demonstrates that neural networks can be used effectively for control of nonlinear dynamical systems. The proposed control scheme is based on the artificial neural network and is applied to an isothermic continuous stirred tank reactor (CSTR). In this paper we have tested the internal model control (IMC) strategy based on neural networks for process systems. This approach of control uses two Feed Forward Neural networks (FFNN), one as an identifier and the other as a controller. Multilayer neural network has been used for forward modeling and the inverse model of the process which has been determined off line using input output data of process, as controller. The modified back propagation algorithm has been used to train the neural networks. Neural network based IMC scheme has been implemented for both set point and regulatory control action and the comparison have been made for a set of constant momentum term.
This paper presents an application of bomb diffusion. This robot moves in four directions like forward, backward, right and left. Here the robot receives the signal from color sensor and then move accordingly. The signal received by robot is in the form of light. This robot contains a camera at the receiver end. The display of the view captured by the camera is on transmitter end which contains LCD. The main application of this robot is to diffuse the metallic bombs which are placed inside the digs and the places where human can't reach. A robotic arm is a type of mechanical arm, usually programmable, with similar functions to a human arm.
An operative and manageable network is always required to fulfill the many practical applications ranges from small scale industry to large scale power conserved environment tracking. In case of wireless sensor networks (WSNs), energy devastation of network node is a tremendous confrontation in the context of maximizing the lifetime span of the WSN. It will always be risky, very costly or in some cases even impossible to charge or replace the overexerted batteries because of the contentious nature of the network. There are many researches for designing energy efficient protocols while as actualizing the desirable WSN operation. We are focusing on many different schemes which are used to degrade the energy utilization factor of the sensing nodes. When we diagnosed the causes for energy dissipation by the nodes, we are able to classify energy efficient schemes such as topology control, duty cycling, control reduction, energy efficient routing and data reduction.
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