Conjugate Gradient Trained Neural Network for Intelligent Sensing of Manhole Gases to Avoid Human Fatality

2014 ◽  
pp. 257-280
Author(s):  
Paramartha Dutta ◽  
Varun Kumar Ojha
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Learning Algorithm ◽  
Real Life ◽  
Nonlinear Problems ◽  
Pipeline System ◽  
Neural Network Learning ◽  
Pattern Recognition Problem ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

Computational Intelligence offers solution to various real life problems. Artificial Neural Network (ANN) has the capability of solving highly complex and nonlinear problems. The present chapter demonstrates the application of these tools to provide solutions to the manhole gas detection problem. Manhole, the access point across sewer pipeline system, contains various toxic and explosive gases. Hence, predetermination of these gases before accessing manholes is becoming imperative. The problem is treated as a pattern recognition problem. ANN, devised for solving this problem, is trained using a supervised learning algorithm. The conjugate gradient method is used as an alternative of back propagation neural network learning algorithm for training of the ANN. The chapter offers comprehensive performance analysis of the learning algorithm used for the training of ANN followed by discussion on the methods of presenting the system result. The authors discuss different variants of Conjugate Gradient and propose two new variants of it.

Neural Q-Learning Based Mobile Robot Navigation

2012 ◽  
Vol 433-440 ◽  
pp. 721-726
Author(s):  
Soh Chin Yun ◽  
S. Parasuraman ◽  
Velappa Ganapathy ◽  
Halim Kusuma Joe
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Mobile Robot Navigation ◽  
Matlab Simulation ◽  
Training Process ◽  
Q Learning ◽  
State Action ◽  
Training Samples ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

This research is focused on the integration of multi-layer Artificial Neural Network (ANN) and Q-Learning to perform online learning control. In the first learning phase, the agent explores the unknown surroundings and gathers state-action information through the unsupervised Q-Learning algorithm. Second training process involves ANN which utilizes the state-action information gathered in the earlier phase of training samples. During final application of the controller, Q-Learning would be used as primary navigating tool whereas the trained Neural Network will be employed when approximation is needed. MATLAB simulation was developed to verify and the algorithm was validated in real-time using Team AmigoBotTM robot. The results obtained from both simulation and real world experiments are discussed.

Research on Improved BP Learning Algorithm of BP Neural Network

2013 ◽  
Vol 765-767 ◽  
pp. 1644-1647 ◽  
Author(s):  
Jian Li Chu ◽  
Hong Yan Li ◽  
Xiao Ji Chen
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Error Function ◽  
Learning Rate ◽  
Good Effect ◽  
Dynamic Learning ◽  
Neural Network Learning ◽  
Hidden Layer

Aiming at the existence of the BP neural network learning algorithm in the slow learning speed, the possibility of failure is large, poor generalization ability, there are multiple issues, extreme value point and network structure are difficult to determine, in this paper, we study algorithm improvement methods. Explain the algorithm principle, on the basis of three improved methods are studied, respectively is dynamic learning rate, conjugate gradient, improved error function. Among them, the dynamic learning rate, it reaches the learning rate of the hidden layer and output layer; Conjugate gradient, this paper gives three calculating formula; Improved error function, to solve different problems are also given in three types of error function. BP learning algorithm in this paper, the research contents, make the convergence stability, convergence speed, initial value sensitivity, it has good effect, which has large significant in terms of academic and applied significance.

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

scholarly journals Nonlinear Aeroelastic System Identification Based on Neural Network

2018 ◽  
Vol 8 (10) ◽  
pp. 1916
Author(s):  
Bo Zhang ◽  
Jinglong Han ◽  
Haiwei Yun ◽  
Xiaomao Chen
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
System Identification ◽  
Control Surface ◽  
Identification Algorithm ◽  
Aeroelastic System ◽  
Structural Nonlinearity ◽  
Time Marching ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

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