scholarly journals Vessel Track Prediction Based on Fractional Gradient Recurrent Neural Network with Maneuvering Behavior Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Han Xue ◽  
Tian Chai
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Traffic Management ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Ship Maneuvering ◽  
Marine Traffic ◽  
Training Error ◽  
Fractional Orders ◽  
And Training

To improve the accuracy of ship track prediction, a fractional-order gradient descent method is adopted into a recurrent neural network (RNN). The convergence of the proposed algorithm is proved. Identification of ship maneuvering behavior, atmospheric information, and oceanographic information is considered in vessel tack prediction. The ship track of Xiamen Port is predicted using the new algorithm. Error analysis is made with different factional orders and traffic busy degrees. Results show that the testing and training error differs with different fractional orders. The predicted track results can not only improve the efficiency of marine traffic management but also prevent and warn the safety accidents, so as to avoid accidents.

scholarly journals Building Recurrent Neural Networks to Implement Multiple Attractor Dynamics Using the Gradient Descent Method

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Namikawa ◽  
Jun Tani
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Transition Function ◽  
Descent Method ◽  
Van Der Pol Oscillator ◽  
Multiple Time ◽  
Gradient Descent Method

The present paper proposes a recurrent neural network model and learning algorithm that can acquire the ability to generate desired multiple sequences. The network model is a dynamical system in which the transition function is a contraction mapping, and the learning algorithm is based on the gradient descent method. We show a numerical simulation in which a recurrent neural network obtains a multiple periodic attractor consisting of five Lissajous curves, or a Van der Pol oscillator with twelve different parameters. The present analysis clarifies that the model contains many stable regions as attractors, and multiple time series can be embedded into these regions by using the present learning method.

scholarly journals A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhiguan Huang ◽  
Zhengtai Xie ◽  
Long Jin ◽  
Yuhe Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Visual Servoing ◽  
Robot Manipulators ◽  
Rapid Evolution ◽  
Inequality Constraints ◽  
Descent Method ◽  
External Information ◽  
Gradient Descent Method ◽  
Gradient Based

Recent decades have witnessed the rapid evolution of robotic applications and their expansion into a variety of spheres with remarkable achievements. This article researches a crucial technique of robot manipulators referred to as visual servoing, which relies on the visual feedback to respond to the external information. In this regard, the visual servoing issue is tactfully transformed into a quadratic programming problem with equality and inequality constraints. Differing from the traditional methods, a gradient-based recurrent neural network (GRNN) for solving the visual servoing issue is newly proposed in this article in the light of the gradient descent method. Then, the stability proof is presented in theory with the pixel error convergent exponentially to zero. Specifically speaking, the proposed method is able to impel the manipulator to approach the desired static point while maintaining physical constraints considered. After that, the feasibility and superiority of the proposed GRNN are verified by simulative experiments. Significantly, the proposed visual servo method can be leveraged to medical robots and rehabilitation robots to further assist doctors in treating patients remotely.

scholarly journals Assess Reliability Parameters of an Electronic Voting Machine using a Neural Network Technique

2020 ◽  
Vol 9 (6) ◽  
pp. 269-275
Keyword(s):  
Neural Network ◽  
Storage System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Descent Method ◽  
Electronic Voting ◽  
Gradient Descent Method ◽  
Neural Network Approach ◽  
Reliability Parameters ◽  
Voting Machine

The structure of Electronic Voting Machine (EVM) is an interconnected network of discrete components that record and count the votes of voters. The EVM system consists of four main subsystems which are Mother board of computer, Voting keys, Database storage system, power supply (AC and DC) along with various conditions of functioning as well as deficiency. The deficiency or failure of system is due to its components (hardware), software and human mismanagement. It is essential to reduce complexity of interconnected components and increase system reliability. Reliability analysis helps to identify technical situations that may affect the system and to predict the life of the system in future. The aim of this research paper is to analyze the reliability parameters of an EVM system using one of the approaches of computational intelligence, the neural network (NN). The probabilistic equations of system states and other reliability parameters are established for the proposed EVM model using neural network approach. It is useful for predicting various reliability parameters and improves the accuracy and consistency of parameters. To guarantee the reliability of the system, Back Propagation Neural Network (BPNN) architecture is used to learn a mechanism that can update the weights which produce optimal parameters values. Numerical examples are considered to authenticate the results of reliability, unreliability and profit function. To minimize the error and optimize the output in the form of reliability using gradient descent method, authors iterate repeatedly till the precision of 0.0001 error using MATLAB code. These parameters are of immense help in real time applications of Electronic Voting Machine during elections.

scholarly journals Adaptive Fuzzy / PD controller for Coupled Two Tank Liquid Levels system

2018 ◽  
Vol 225 ◽  
pp. 06020
Author(s):  
Ahmed N Abdalla ◽  
Thamir K Ibrahim ◽  
Hai Tao
Keyword(s):  
Neural Network ◽  
Spray Coating ◽  
Adaptive Controller ◽  
Descent Method ◽  
Fine Tuning ◽  
Effluent Treatment ◽  
Control Technique ◽  
Response Analysis ◽  
Gradient Descent Method ◽  
Fuzzy Adaptive

Liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. A typical situation is one that requires fluid to be supplied to a tank at a constant rate which has Two-Input Two-Output (TITO). The accuracy and stability of TITO is one of the key factors of process which have cross coupling between process input and output. Unlike traditional neural network weight adaptation using gradient descent method, Fuzzy Adaptive PID control technique was utilized for adaptive tuning and fine tuning the controller’s parameters. Design approach for controlling liquid levels of Coupled Tank TITO system by using Fuzzy Adaptive PID controllers. Tuning method for parameters of improved Fuzzy Adaptive, Particle Swamp Optimization and Neural network controllers ware also discussed. Experimental results demonstrate that the performances of proposed method perform well compared with convention Fuzzy and Tuned Fuzzy controllers; it was shown that Fuzzy Adaptive controller exhibited better performance in terms of transient response analysis.

scholarly journals Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jianyong Liu ◽  
Huaixiao Wang ◽  
Yangyang Sun ◽  
Chengqun Fu ◽  
Jie Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Search Space ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Quantum Genetic Algorithm ◽  
Binary Coding ◽  
Optimal Value ◽  
Neural Network Algorithm

The method that the real-coded quantum-inspired genetic algorithm (RQGA) used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA) is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

scholarly journals Flood Detection from Satellite Images Based on Deep Convolutional Neural Network and Layered Recurrent Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 2041-2045
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Satellite Images ◽  
Satellite Image ◽  
High Accuracy ◽  
Training Phase ◽  
Training Set ◽  
Flood Detection ◽  
And Training

Satellite images are important for developing and protected environmental resources that can be used for flood detection. The satellite image of before-flooding and after-flooding to be segmented and feature with integration of deeply LRNN and CNN networks for giving high accuracy. It is also important for learning LRNN and CNN is able to find the feature of flooding regions sufficiently and, it will influence the effectiveness of flood relief. The CNNs and LRNNs consists of two set are training set and testing set. The before flooding and after flooding of satellite images to be extract and segment formed by testing and training phase of data patches. All patches are trained by LRNN where changes occur or any misdetection of flooded region to extract accurately without delay. This proposed method obtain accuracy of system is 99% of flood region detections.

scholarly journals Hierarchical Quasi-Fractional Gradient Descent Method for Parameter Estimation of Nonlinear ARX Systems Using Key Term Separation Principle

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3302
Author(s):  
Naveed Ishtiaq Chaudhary ◽  
Muhammad Asif Zahoor Raja ◽  
Zeshan Aslam Khan ◽  
Khalid Mehmood Cheema ◽  
Ahmad H. Milyani
Keyword(s):  
Parameter Estimation ◽  
Gradient Descent ◽  
Identification Problem ◽  
Descent Method ◽  
Estimation Accuracy ◽  
Gradient Descent Method ◽  
Hierarchical Identification ◽  
System A ◽  
Nonlinear Autoregressive ◽  
Fractional Orders

Recently, a quasi-fractional order gradient descent (QFGD) algorithm was proposed and successfully applied to solve system identification problem. The QFGD suffers from the overparameterization problem and results in estimating the redundant parameters instead of identifying only the actual parameters of the system. This study develops a novel hierarchical QFDS (HQFGD) algorithm by introducing the concepts of hierarchical identification principle and key term separation idea. The proposed HQFGD is effectively applied to solve the parameter estimation problem of input nonlinear autoregressive with exogeneous noise (INARX) system. A detailed investigation about the performance of HQFGD is conducted under different disturbance conditions considering different fractional orders and learning rate variations. The simulation results validate the better performance of the HQFGD over the standard counterpart in terms of estimation accuracy, convergence speed and robustness.

Fault diagnosis method of mine hoist braking system based on deep convolution neural network

2021 ◽  
Author(s):  
Fangyuan Yan ◽  
Juanli Li ◽  
Dong Miao ◽  
Qi Cao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Descent Method ◽  
Convolution Neural Network ◽  
Monitoring Data ◽  
Gradient Descent Method ◽  
Braking System ◽  
Diagnosis Method ◽  
Mine Hoist

Abstract A reliable braking system is an important guarantee for safe operation of mine hoist. In order to make full use of the monitoring data in the operation process of mine hoist, identify the operation status of the hoist, and further carry out fault diagnosis on it, the deep learning method was introduced into the fault diagnosis of the hoist, and a fault diagnosis method of hoist braking system based on convolution neural network has been proposed. Firstly, the working principle and fault mechanism of disc brake and its hydraulic station in hoist braking system are analyzed, and the monitoring parameters of this study are determined; then, based on massive monitoring data, the convolutional neural networks (CNN) is established, the one-dimensional signal collected by the sensor is transformed into two-dimensional image for coding, the neural network is trained by gradient descent method, and the network structure parameters are modified according to the training results. Finally, the fault diagnosis model is compared and verified by using the sample set based on the traditional back propagation neural network (BP) and CNN. The results show that the accuracy of CNN is higher than that of BP, and the accuracy rate can reach 99.375% after reducing the involvement between samples. This method can make full use of the monitoring data for diagnosis, without subjective intervention of experts, and improve the accuracy of diagnosis.

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