A Solution for Two-Dimensional Mazes with Use of Chaotic Dynamics in a Recurrent Neural Network Model

2004 ◽  
Vol 16 (9) ◽  
pp. 1943-1957 ◽  
Author(s):  
Yoshikazu Suemitsu ◽  
Shigetoshi Nara
Keyword(s):  
Neural Network ◽  
State Space ◽  
Network Model ◽  
Neural Network Model ◽  
Chaotic Dynamics ◽  
Dimensional Space ◽  
Dynamical Structure ◽  
Two Dimensional ◽  
Time Step ◽  
The Neural Network

Chaotic dynamics introduced into a neural network model is applied to solving two-dimensional mazes, which are ill-posed problems. A moving object moves from the position at t to t + 1 by simply defined motion function calculated from firing patterns of the neural network model at each time step t. We have embedded several prototype attractors that correspond to the simple motion of the object orienting toward several directions in two-dimensional space in our neural network model. Introducing chaotic dynamics into the network gives outputs sampled from intermediate state points between embedded attractors in a state space, and these dynamics enable the object to move in various directions. System parameter switching between a chaotic and an attractor regime in the state space of the neural network enables the object to move to a set target in a two-dimensional maze. Results of computer simulations show that the success rate for this method over 300 trials is higher than that of random walk. To investigate why the proposed method gives better performance, we calculate and discuss statistical data with respect to dynamical structure.

Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network Combined Neural Network

2016 ◽  
Vol 13 (10) ◽  
pp. 7074-7079
Author(s):  
Yajun Xu ◽  
Fengmei Liang ◽  
Gang Zhang ◽  
Huifang Xu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Network Model ◽  
Neural Network Model ◽  
Gabor Wavelet ◽  
Two Dimensional ◽  
Human Face ◽  
Gabor Wavelet Transform ◽  
Gabor Function ◽  
The Neural Network

This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed to extract the image feature information. Based on the BP neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, when the method combining Gabor wavelet transform and the neural network is used to test the human face, it will not influence the detection results despite of complex textures and illumination variations on face images. Besides, when ORL human face database is used to test the model, the human face detection accuracy can reach above 0.93.

SENSITIVE RESPONSE OF A CHAOTIC WANDERING STATE TO MEMORY FRAGMENT INPUTS IN A CHAOTIC NEURAL NETWORK MODEL

2004 ◽  
Vol 14 (04) ◽  
pp. 1413-1421 ◽  
Author(s):  
JOUSUKE KUROIWA ◽  
NAOKI MASUTANI ◽  
SHIGETOSHI NARA ◽  
KAZUYUKI AIHARA
Keyword(s):  
Neural Network ◽  
State Space ◽  
Network Model ◽  
Neural Network Model ◽  
Chaotic Dynamics ◽  
External Input ◽  
The Other ◽  
Chaotic Neural Network ◽  
Dynamical Properties ◽  
Memory Pattern

Dynamical properties of a chaotic neural network model in a chaotically wandering state are studied with respect to sensitivity to weak input of a memory fragment. In certain parameter regions, the network shows weakly chaotic wandering, which means that the orbits of network dynamics in the state space are localized around several memory patterns. In the other parameter regions, the network shows highly developed chaotic wandering, that is, the orbits become itinerant through ruins of all the memory patterns. In the latter case, once the external input consisting of a memory fragment is applied to the network, the orbit quickly moves to the vicinity of the corresponding memory pattern including the memory fragment within several iteration steps. Thus, chaotic dynamics in the model is effective for instantaneous search among memory patterns.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Linear Static Response of Suspension Arm Based on Artificial Neural Network Technique

2011 ◽  
Vol 213 ◽  
pp. 419-426
Author(s):  
M.M. Rahman ◽  
Hemin M. Mohyaldeen ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Mesh Size ◽  
Maximum Displacement ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis

Modeling and simulation are indispensable when dealing with complex engineering systems. This study deals with intelligent techniques modeling for linear response of suspension arm. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique is used to predict the response of suspension arm. The linear static analysis was performed utilizing the finite element analysis code. The neural network model has 3 inputs representing the load, mesh size and material while 4 output representing the maximum displacement, maximum Principal stress, von Mises and Tresca. Finally, regression analysis between finite element results and values predicted by the neural network model was made. It can be seen that the RBFNN proposed approach was found to be highly effective with least error in identification of stress-displacement of suspension arm. Simulated results show that RBF can be very successively used for reduction of the effort and time required to predict the stress-displacement response of suspension arm as FE methods usually deal with only a single problem for each run.

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