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.

Incursion detection system using the hybrid neural network and incursion dectection method using the same

Abstract: PURPOSE: A system and a method for detecting intrusion using a hybrid neural network are provided to detect the intrusion including an unknown intrusion pattern and to process the intrusion in real-time. CONSTITUTION: A packet collector(110) collects a packet existed on the network. A packet preprocessor(120) patterns the collected packet through a preprocessing process in order to use the packet collected through the packet collector as an input value of the neural network. An intrusion detection pattern learning part(210) learns the patterned packet by receiving the patterned packet from the packet preprocessor and using the clustering neuron network, and clusters the intrusion detection pattern by using a data distribution and a frequency. An intrusion detection judging part(220) receives a clustering result value and the connection level information of the patterned packet, learns the intrusion detection judgment through the result value and the connection level information by using the learning neuron network, and detects the intrusion.

Two-value image data compressing and recovering using improved neural network

Abstract: Data compression and generalization capability are important characteristics of a neural network model. From this point of view, the two-value image data compression and recovering of a hybrid neural network are examined experimentally. The applied neural network models are the improved ART1 and the feedforward types. The hybrid network architecture, its learning process and the improved learning algorithm are presented in this paper. The whole work has been finished using a large scale general-purpose neural network simulating system, the GKD-N/sup 2/S/sup 2/ on the SUN3 workstation. Some experimental results also have been given and are discussed. >

Application of Wolf Swarm Neural Network in Surface Reconstruction

Abstract: Although the traditional BP neural network has strong nonlinear fitting ability, it has poor global search ability, slow convergence speed and easy to be trapped into local minimum value, etc. Based on this, a WPA-BP hybrid neural network surface reconstruction algorithm combining the Wolf pack algorithm and BP algorithm is proposed. WPA-BP hybrid algorithm has both the adaptive ability of BP algorithm and the global optimization ability of WPA algorithm, which can improve the existing problems of BP algorithm model. Compared with the reconstruction results based on BP algorithm and quadratic fitting algorithm, the surface reconstruction model using WPA-BP hybrid algorithm has higher reconstruction accuracy and smaller reconstruction error.

Fuzzy Systems and Neural Networks

Abstract: Research and development in the use of fuzzy systems with neural networks has been proceeding at a rapid pace during the last few years and applications are starting to be developed. A natural integration follows the same successful path of hybrid neural network and expert systems by using fuzzy rule based systems instead of traditional expert systems. Thus all the various ways of integration are available for fuzzy connectionist systems. Additionally, neural networks can be used as tools for designing and tuning fuzzy systems. And, fuzzy principles can be used in the design of neural networks, embedding fuzziness in the internal workings of the basically neural system.

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

Abstract: The use of a proposed recurrent neural net- work 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 con- troller, and the walking robot. The robot is a planar four-legged walking robot. The proposed Neural Net- work (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 feed- forward 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 ro- bot'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 distur- bances. Also, the PD controller alone and Computed Torque Method (CTM) control system are used to con- trol the walking robot's position for comparison.