A Study on Solving the Inverse Kinematics of Serial Robots using Artificial Neural Network and Fuzzy Neural Network

2019 ◽  
Author(s):  
Jacket Demby's ◽  
Yixiang Gao ◽  
G. N. DeSouza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Kinematics ◽  
Fuzzy Neural Network ◽  
Serial Robots ◽  
Fuzzy Neural ◽  
Artificial Neural

Freeway Slope Stability Evaluation Based on Multi-Level Fuzzy Neural Network

2012 ◽  
Vol 430-432 ◽  
pp. 1700-1703
Author(s):  
Yan Kai Wu ◽  
Xian Song Sang ◽  
Bin Niu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Slope Stability ◽  
Basic Principle ◽  
Fuzzy Neural Network ◽  
Stability Assessment ◽  
High Slope ◽  
Fuzzy Neural ◽  
Multi Level ◽  
Artificial Neural

On the basis of introduced basic principle of fuzzy-artificial neural network, this article constructed a slope stability assessment index system with multi-level fuzzy neural network, and made detailed evaluation criterion according to the assessment characteristics of slope stability. Through introducing the basic principle of multi-level comprehensive assessment from fuzzy mathematics and artificial neural network theory, it overcomes the defect of difficult to be quantified in evaluation process of slope stability. Therefore, it can be better to deal with some uncertain problems occurred in the slope stability assessment process, and as much as possible to express all factors influencing slope stability really and objectively. We selected 20 single factor evaluation indexes to assess slope stability based on surveying the high slope stability in Mian county-Ningqiang county freeway section. It took "normal distribution model function" as a membership function to develop a program with the model of fuzzy neural network. Furthermore, we took 30 typical slope examples as training sample to conduct effectiveness test and feedback test for the program. After the precision requirement was met, we used the program to evaluate 21 high slope examples and compared the results with the ones solved by traditional mechanical methods. The coincidence degree by using two kinds of methods to assess the same slope stability is 76.2%.

Performance Prediction Using Fuzzy Mapping and Artificial Neural Network

2014 ◽  
Vol 539 ◽  
pp. 901-903
Author(s):  
Feng Yu ◽  
Yi Wu ◽  
Xian Ming Shao ◽  
Jian Chun Guo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Performance Prediction ◽  
Fuzzy Neural Network ◽  
Good Precision ◽  
Fuzzy Mapping ◽  
Model Combining ◽  
Fuzzy Neural ◽  
Artificial Neural ◽  
Comparative Validation

This paper presents a fuzzy neural network model combining fuzzy mapping and artificial neural network for the prediction of sports results. The model for predicting track & field results of each individual event at the 27th Olympic Games is established. Through modeling and comparative validation it is shown that since 1950s the modeling of track & field results according to the sequence of number reflects the basic trends of the track & field result development with good precision.

Face Recognition Method Based on the Fuzzy Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 134-137
Author(s):  
Xiang Pan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Fuzzy Neural Network ◽  
Experimental Results ◽  
Fuzzy Rules ◽  
Good Effect ◽  
Recognition Method ◽  
Fuzzy Neural ◽  
Artificial Neural

This paper discusses a face recognition method based on the fuzzy neural network (FNN). The fuzzy neural network has more advantages than artificial neural network alone. The paper firstly introduces the structure of the FNN. Than proposed the fuzzy rules and the study algorithm. Thirdly it researches on the process of face recognition. The experimental results prove that this method can achieve good location performance and good effect of extraction.

scholarly journals Artificial Neural Network and Fuzzy Neural Network Algorithm for Financial Health Analysis of Indonesian SOEs

2018 ◽  
Vol 2 (1) ◽  
pp. 63-70
Author(s):  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Neural Network ◽  
Financial Health ◽  
Network Algorithm ◽  
Fitness Value ◽  
Fuzzy Neural ◽  
Financial Soundness ◽  
Neural Network Algorithm ◽  
Artificial Neural

The determining of financial soundness of SOEs company is regulated by the government through Decree of the Minister of SOEs KEP: 100 / BUMN / 2002. There are 8 parameters to be calculated for determining financial soundness such as ROE, ROI, cash ratio, current ratio, collection periods, inventory turnover, TATO, and ratio of total equity to total assets. From the calculation results based on these rules, there are 3 categories of companies, that is healthy, less healthy and unhealthy. To calculate the best parameters as a significant aspect to determining financial soundness, this research using neural networks method. In this paper, it compares the value of accuracy and learning rate with Artificial Neural Network and Fuzzy Neural Network method. Accuracy used as the fitness value of the Genetic Algorithm, to get the top three parameters from eight parameters to determining the financial soundness of SOEs companies. The result of this research both ANN and FNN get the same top three parameters: ROE, ROI, and Cash Ratio. In overall, artificial neural network or fuzzy neural network algorithm both suitable for use in the financial health analysis of SOEs companies.

scholarly journals Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

2015 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Panchand Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural ◽  
Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

scholarly journals Experimental Validation for Inverse Kinematics of a Five Axis Robotic Manipulator using Deep Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 3943-3946
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Kinematics ◽  
Human Life ◽  
Robotic Manipulator ◽  
Training Dataset ◽  
Ann Model ◽  
Line Spacing ◽  
Artificial Neural ◽  
Five Axis

The use of robotics is to improvise and simplify human life. Robotic manipulators have been around for a while now and are being used in many different sectors such as industries, households, warehouses, medicine etc. Solving of inverse kinematics is one of the most complex issues faced while designing the robotic manipulator. In this research a Deep Artificial neural network (D-ANN) model is proposed to solve inverse kinematics of a 5-axis robotic manipulator with rotary joints. The D-ANN model is trained in MATLAB. Training dataset was generated using forward kinematics equations obtained easily from transformation matrix of the robotic manipulator. To validate predictions made by this model an experimental robotic arm manipulator Is fabricated. A smart camera setup has been linked to MATLAB for real time image processing and calculating the deviation of the end needle in reaching the desired target coordinate. The trained model yielded satisfactory results with ±0.03 radians error and this was also validated experimentally. This research will help the robotic manipulator reach the desired target coordinates even when one does not have enough input data.Paper Setup must be in A4 size with Margin: Top 0.7”, Bottom 0.7”, Left 0.65”, 0.65”, Gutter 0”, and Gutter Position Top. Paper must be in two Columns after Authors Name with Width 8.27”, height 11.69” Spacing 0.2”. Whole paper must be with: Font Name Times New Roman, Font Size 10, Line Spacing 1.05 EXCEPT Abstract, Keywords (Index Term), Paper Tile, References, Author Profile (in the last page of the paper, maximum 400 words), All Headings, and Manuscript Details (First Page, Bottom, left side).

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