Hybrid neural network

About: Hybrid neural network is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 18223 citations.

An efficient hybrid neural network model in renewable energy systems

Abstract: This paper presents a hybrid neural network approach to predict wind speed automatically in renewable energy systems. Wind energy is one of the renewable energy systems with lowest cost of production of electricity with largest resources available. By the reason of the fluctuation and volatility in wind, the wind speed prediction provides the challenges in the stability of renewable energy system. The aim is to compute predicted wind speed based on hybrid model which integrates a Self Organizing Map (SOM) and Radial basis Function (RBF) neural network. The simulation result shows that the proposed approach provides better result of wind speed prediction with less error rates.

Design of slot loaded proximity coupled microstrip antennas using knowledge based neural networks

Abstract: In this paper, a knowledge based hybrid neural network (KBHNN) is utilized for designing of different slotted proximity coupled microstrip antennas. The slot loaded antennas can be designed from 1 to 6 GHz frequency ranges. By using this model, accuracy is found to be really beneficial, even if the required number of training data has been brought down to half. This method requires less time and scales down the complexities of the design processes. The solutions obtained by this neural approach are compared with the CST simulation results. The results of the KBHNN method are in good accord with the simulated values.

Neural network dimension selection for dynamical system identification

Abstract: Choosing an appropriate size of a network is an important issue for any neural network applications. The common practice is to start with an ldquoover-sizedrdquo network, then gradually reduces its size to find the optimal solution. In this paper, a new hybrid neural network pruning algorithm for multi-layer feedforward neural networks is investigated. Computer simulation results on system identification and pattern classification problems show this algorithm can significantly reduce the network dimension while still maintaining satisfactory identification and classification accuracy.

A proposed hybrid neural network for position control of a walking robot

Abstract: The use of a proposed recurrent neural network control system to control a four-legged walking robot is presented in this paper. The control system consists of a neural controller, a standard PD controller, and the walking robot. The robot is a planar four-legged walking robot. The proposed Neural Network (NN) is employed as an inverse controller of the robot. The NN has three layers, which are input, hybrid hidden and output layers. In addition to feedforward connections from the input layer to the hidden layer and from the hidden layer to the output layer, there is also a feedback connection from the output layer to the hidden layer and from the hidden layer to itself. The reason to use a hybrid layer is that the robot’s dynamics consists of linear and nonlinear parts. The results show that the neural-network controller can efficiently control the prescribed positions of the stance and swing legs during the double stance phase of the gait cycle after sufficient training periods. The goal of the use of this proposed neural network is to increase the robustness of the control of the dynamic walking gait of this robot in the case of external disturbances. Also, the PD controller alone and Computed Torque Method (CTM) control system are used to control the walking robot’s position for comparison.

Fuzzy self-organizing hybrid neural network for gas analysis system

Abstract: The paper presents the gas analysis system applying the self-organizing fuzzy hybrid neural network. The network is composed of the self-organizing competitive fuzzy layer and the supervised multilayer perceptron (MLP) subnetwork, connected in cascade. The characteristic features of this network structure for gas analysis systems are discussed and the results of experiments compared to standard neural solutions based on MLP or classical hybrid network employing the Kohonen layer.