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.

Intrusion detection using PCASOM neural networks

Abstract: This paper proposes a method to detect network intrusions by using the PCASOM (principal components analysis and self-organizing map) neural networks. A modified unsupervised learning algorithm which is more suitable for intrusion detection is presented. Experiments are carried out to illustrate the performance of the proposed method by using DARPA 1998 evaluation data sets. It shows that the proposed method can cluster the network connections into proper clusters with high detection rate and relatively low false alarm rate.

Efficient Hybrid Neural Network for Chaotic Time Series Prediction

Abstract: We propose an efficient hybrid neural network for chaotic time series prediction. The hybrid neural network is constructed by a traditional feed-forward network, which is learned by using the backpropagation and a local model, which is implemented as a time delay embedding. The feed-forward network performs as the global approximation and the local model works as the local approximation. Experimental results using Mackey-Glass data and K.U. Leuven competition data show that the proposed method can predict the more long term than each of predictors.

A Novel Hybrid Neural Network-Based Multirobot Path Planning With Motion Coordination

Abstract: Multi-robot navigation is a challenging task, especially for many robots, since individual gains may more often than not adversely affect the global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace whereas the motion of every individual robot is deduced by a novel Particle Swarm Optimization (PSO) tuned Feed Forward Neural Network (FFNN). Motion coordination among the robots is implemented using a cooperative coordination algorithm that identifies critical robots and maintains cooperation count while actuating deviation in select robots. The contribution of this paper is twofold; firstly in hybridizing the Artificial Neural Network(ANN) by employing PSO, an evolutionary algorithm, to find optimal values of deviation for every critical robot using velocity and acceleration constraints, secondly ensuing the convergence of the PSO by carrying first and second order stability analysis. Experiments have been carried out to evaluate and validate the efficacy of the proposed coordination schemes by changing the number of robots under hundred different scenarios each, and the founded results demonstrate the efficacy of the proposed schemes.

Evolutionary Approach for Mobile Robot Path Planning in Complex environment

Abstract: The shortest/optimal path planning in a static environment is essential for the efficient operation of a mobile robot. Recent advances in robotics and machine intelligence have led to the application of modern optimization method such as the genetic algorithm (GA), to solve the path-planning problem. In this paper, the problem of finding the optimal collision free path in complex environments for a mobile robot is solved using a hybrid neural network, Genetic Algorithm and local Search method. We constructed the neural network model of environmental and used this model to establish the relationship between a collision avoidance path and the output of the model. What is new in this work is a novel representation of solutions for evolutionary algorithms that is efficient, simple and also compatible with Hybrid algorithm. The new representation makes it possible to solve the problem with a small population and in a few generations. It also makes the genetic operator simple and allows using an efficient local search operator within the evolutionary algorithm. The performance of the proposed GA approach is tested on eight different environments consisting of polygonal obstacles with increasing complexity.