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 enhanced Counter Propagation Neural Network for abnormal retinal image classification

Abstract: Artificial Neural Networks (ANN) is gaining significant importance for pattern recognition applications particularly in the medical field. A hybrid neural network such as Counter Propagation Neural Network (CPN) is highly desirable since it comprises the advantages of supervised and unsupervised training methodologies. Even though it guarantees high accuracy, the network is computationally non-feasible. This drawback is mainly due to the high convergence time period. In this paper, a modified Counter Propagation Neural Network is proposed to tackle this problem which eliminates the iterative training methodology which accounts for the high convergence time. To prove the efficiency, this technique is employed on abnormal retinal image classification system. Real time images from four abnormal classes are used in this work. An extensive feature vector is framed from these images which forms the input for the CPN and the modified CPN. The experimental results of both the networks are analyzed in terms of classification accuracy and convergence time period. The results suggest the superior nature of the proposed technique in terms of convergence time period and classification accuracy.

Hybrid neural network under-actuated sliding-mode control for trajectory tracking of quad-rotor unmanned aerial vehicle

Abstract: Due to the under-actuated characteristic of quadrotor unmanned aerial vehicle (QUAV), the sliding surface using measurable output (i.e., 3D position and attitude), whose number is larger than that of control input (i.e., total thrust force, roll, pitch and yaw torques), is designed. Hence, the number of control input and sliding surface is the same, and the indirectly controlled mode (e.g., x- and y-axes) is controlled. Under uncertain environment, the sliding-mode under-actuated control (SMUC) with suitable conditions is first derived so that asymptotical and bounded tracking results are achieved. To improve system performance, an on-line recurrent neural network modeling for dynamical uncertainty of QUAV is employed to design a recurrent-neural-network-based sliding-mode under-actuated control (RNNSMUC). Then the proposed hybrid neural-network-based sliding-mode under-actuated control (HNNSMUC) combining SMUC and RNNSMUC with a transition maintains both advantages of SMUC and RNNSMUC and simultaneously avoids the disadvantages coming from SMCU and RNNSMUC.

Inverse Kinematic Solution of Robot Manipulator Using Hybrid Neural Network

Abstract: Inverse kinematics of robot manipulator is to determine the joint variables for a given Cartesian position and orientation of an end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Although artificial neural network (ANN) can be gainfully used to yield the desired results but the gradient descent learning algorithm does not have ability to search for global optimum and it gives slow convergence rate. This paper proposes structured ANN with hybridization of Gravitational Search Algorithm to solve inverse kinematics of 6R PUMA robot manipulator. The ANN model used is multi-layered perceptron neural network (MLPNN) with back-propagation (BP) algorithm which is compared with hybrid multi layered perceptron gravitational search algorithm (MLPGSA). An attempt has been made to find the best ANN configuration for the problem. It has been observed that MLPGSA gives faster convergence rate and improves the problem of trapping in local minima. It is found that MLPGSA gives better result and minimum error as compared to MLPBP.

A Hybrid Neural Network Method for UAV Attack Route Integrated Planning

Abstract: This paper proposes a hybrid neural network method to solve the UAV attack route planning problem considering multiple factors. In this method, the planning procedure is decomposed by two planners: penetration planner and attack planner. The attack planner determines a candidate solution set, which adopts Guassian Radial Basis Function Neural Networks (RBFNN) to give a quick performance evaluation to find the optimal candidate solutions. The penetration planner adopts an alterative Hopfield Neural Network (NN) to refine the candidates in a fast speed. The combined effort of the two neural networks efficiently relaxes the coupling in the planning procedure and is able to generate a near-optimal solution within low computation time. The algorithms are simple and can easily be accelerated by parallelization techniques. Detailed experiments and results are reported and analyzed.

Rendezvous points based energy-aware routing using hybrid neural network for mobile sink in wireless sensor networks

Abstract: In wireless sensor networks (WSN), the data are collected from the sensor using the mobile sink for preventing the energy-hole or hotspot problem through traversing the network periodically. The mobile sink permits the node to visit only the fewest number of nodes or locations called rendezvous points (RPs) to minimize the energy utilization and delay by visiting all the cluster heads (CHs). Further, the CHs transmit the packets to its adjacent RP. Several approaches are employed for enhancing the network lifetime and reducing the energy utilization. This paper presents a new hybrid neural network based energy-efficient routing strategy through RPs. Initially, the sensor nodes are clustered utilizing the mean shift clustering methodology. Then, the new Bald Eagle Search algorithm selects the cluster head (CH) for the clustered nodes. Consequently, RPs are selected instead of visiting all the cluster heads. Here, RPs are elected based on the weights evaluation among number of transmitted data packets and hop distance. Finally, a hybrid neural network with Group Teaching Algorithm is introduced to determine the best path through the selected RPs that moderates the energy utilization in WSNs. The implementation of the introduced methodology is performed in the Matlab platform. The simulation results proves that the presented methodology provides better outcomes than the previous techniques in regards of energy utilization, throughput, packet delivery ratio, delay, packet loss ratio, jitter, latency and network lifetime.