Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 433-444 ◽  
Author(s):  
A. S. Morris ◽  
M. A. Mansor
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Network ◽  
Radial Basis

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

scholarly journals Development of combined back propagation algorithm and radial basis function for diagnosing depression patients

2015 ◽  
Vol 4 (1) ◽  
pp. 244
Author(s):  
Bhuvana R. ◽  
Purushothaman S. ◽  
Rajeswari R. ◽  
Balaji R.G.
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Optimum Number ◽  
Back Propagation Algorithm ◽  
Efficient System ◽  
Propagation Algorithm ◽  
Radial Basis

Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.

Intelligent Parallel Networks for Combustion Quality Monitoring in Power Station Boilers

2013 ◽  
Vol 699 ◽  
pp. 893-899 ◽  
Author(s):  
K. Sujatha ◽  
N. Pappa ◽  
U. Siddharth Nambi ◽  
C.R. Raja Dinakaran ◽  
K. Senthil Kumar
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Research Work ◽  
Back Propagation ◽  
Average Intensity ◽  
Back Propagation Algorithm ◽  
Feature Size ◽  
Propagation Algorithm ◽  
Radial Basis

This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by combining Radial Basis Function Network (RBF) and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler so as to control the air/fuel ratio. For this two dimensional flame images are required which was captured with CCD camera whose features of the images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF. Also three classes of images corresponding to different burning conditions of the flames have been extracted from a continuous video processing. In this the corresponding temperatures, the Carbon monoxide (CO) emissions and other flue gases have been obtained through measurement. Further the training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data collected have been done and the performance of the algorithms is presented.

A Radial Basis Function Neural Network Approach To Determine the Survival of Listeria monocytogenes in Katiki, a Traditional Greek Soft Cheese

2008 ◽  
Vol 71 (4) ◽  
pp. 750-759 ◽  
Author(s):  
EFSTATHIOS Z. PANAGOU
Keyword(s):  
Neural Network ◽  
Listeria Monocytogenes ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Approach ◽  
F Test ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Radial Basis ◽  
Modified Weibull

A radial basis function neural network was developed to determine the kinetic behavior of Listeria monocytogenes in Katiki, a traditional white acid-curd soft spreadable cheese. The applicability of the neural network approach was compared with the reparameterized Gompertz, the modified Weibull, and the Geeraerd primary models. Model performance was assessed with the root mean square error of the residuals of the model (RMSE), the regression coefficient (R2), and the F test. Commercially prepared cheese samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 106 CFU g −1 and stored at 5, 10, 15, and 20°C for 40 days. At each storage temperature, a pathogen viability loss profile was evident and included a shoulder, a log-linear phase, and a tailing phase. The developed neural network described the survival of L. monocytogenes equally well or slightly better than did the three primary models. The performance indices for the training subset of the network were R2 = 0.993 and RMSE = 0.214. The relevant mean values for all storage temperatures were R2 = 0.981, 0.986, and 0.985 and RMSE = 0.344, 0.256, and 0.262 for the reparameterized Gompertz, modified Weibull, and Geeraerd models, respectively. The results of the F test indicated that none of the primary models were able to describe accurately the survival of the pathogen at 5°C, whereas with the neural network all f values were significant. The neural network and primary models all were validated under constant temperature storage conditions (12 and 17°C). First or second order polynomial models were used to relate the inactivation parameters to temperature, whereas the neural network was used a one-step modeling approach. Comparison of the prediction capability was based on bias and accuracy factors and on the goodness-of-fit index. The prediction performance of the neural network approach was equal to that of the primary models at both validation temperatures. The results of this work could increase the knowledge basis for the applicability of neural networks as an alternative tool in predictive microbiology.

scholarly journals Regulation capability evaluation of individual electric heating load based on Radial basis Function neural network

2019 ◽  
Vol 23 (5 Part A) ◽  
pp. 2821-2829 ◽  
Author(s):  
Liwei Zhang ◽  
Xiaotian Liu ◽  
Jingbiao Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Evaluation Method ◽  
Back Propagation ◽  
Electric Heating ◽  
Northern Region ◽  
Thermal Parameters ◽  
Radial Basis ◽  
Heating Load

As a time-shifting load that is gradually popularized in the northern region, electric heating load has great adjustment potential. Because the electric heating operation characteristics are affected by many non-linear factors, the traditional equivalent thermal parameters model cannot accurately evaluate the regulation capability of individual electric heating load. Aiming at this problem, this paper proposes an evaluation method for the regulation capability of individual electric heating load based on radial basis function neural network. Firstly, electric heating load control experiments were carried out in a typical room of a residential quarter in winter and relevant experimental data were collected. Then, based on the operation data, the radial basis function neural network is used to evaluate the regulation capability of the individual electric heating load. Finally, the evaluation results based on radial basis function neural network are compared with those based on back propagation neural network and equivalent thermal parameters model. The results show that the proposed method has the least evaluation error and can more accurately evaluate the regulation capability of individual electric heating load.

Intelligent Radial Basis Function Neural Network for Intrusion Detection in Battle Field

2018 ◽  
pp. 151-173
Author(s):  
Kirupa Ganapathy
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Radial Basis Function ◽  
Learning Process ◽  
Basis Function ◽  
Detection System ◽  
The Neural Network ◽  
Training Samples ◽  
Radial Basis

Defense at boundary is nowadays well equipped with perimeter protection, cameras, fence sensors, radars etc. However, in battlefield there is more feasibility of entering of a non-native human and unknowing stamping of the explosives placed in the various paths by the native soldiers. There exists no alert system in the battlefield for the soldiers to identify the intruder or the explosives in the field. Therefore, there is a need for an automated intelligent intrusion detection system for battlefield monitoring. This chapter proposes an intelligent radial basis function neural network (RBFNN) technique for intrusion detection and explosive identification. The proposed intelligent RBFNN implements some intellectual components in the algorithm to make the neural network think before learning the training samples. Involvement of intellectual components makes the learning process simple, effective and efficient. The proposed technique helps to reduce false alarm and encourages timely detection thereby providing extensive support for the native soldiers and save the life of the mankind.

SMART RISK ANALYST

2019 ◽  
Author(s):  
Dr. Shilpa Laddha-Kabra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Credit Risk ◽  
Radial Basis Function ◽  
Basis Function ◽  
Back Propagation ◽  
Arbitrary System ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This book is an expert system for analyzing credit risk in consumer loan using Artificial Neural Network (ANN). When an individual needs to borrow money, the lender will not only expect repayment but will also want to have confidence that the amount lent can be repaid on time. The effort by the borrower to provide the lender with this confidence level will depend on the amount lent. For lending millions of dollars, the lender may want to take a security interest in assets that have a value in excess of the amount lent to cover fluctuations in the values of those assets during the time the loan is being repaid. When time and foresight permit advance arrangement of loans, the act of borrowing can be made much simpler. When time is short and the need for the loan was not anticipated, the act of going through the process of borrowing may be so time-consuming that obtaining the loan may not be possible at all. Radial Basis Function (RBF), Recurrent Neural Network (RNN), and Back propagation or Multilayer Perceptron (MLP) are the three most popular Artificial Neural Network (ANN) tool for the prediction task. Here the author used both feed forward neural network and radial basis function neural network, back propagation algorithm to make the credit risk prediction. The network can be trained with available data to model an arbitrary system. The trained network is then used to predict the risk in granting the loan. ABOUT THE AUTHOR Dr. Shilpa Laddha-Kabra is Assistant Professor in the Department of Information Technology at Government College of Engineering, Aurangabad (Maharashtra). She is Doctorate (Ph.D.) in Computer Science and Engineering. Her area of interest includes Neural Networks, Information Retrieval, Semantic Web Mining & Ontology and many more. She has a profound expertise in taking the full depth training of engineering students. She has Two Copyrights to her credit & her many research papers are published in prominent international journals.

Application of Radial Basis Function Neural Network on the Prediction of Urban Built-Up Area

2014 ◽  
Vol 556-562 ◽  
pp. 5308-5311
Author(s):  
Li Hua Chen ◽  
Yu Chen Wang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Quantitative Prediction ◽  
Basic Issue ◽  
Radial Basis ◽  
Neural Network Prediction ◽  
Network Prediction

The study on the prediction of urban built-up area is the basic issue in urban planning. This paper takes the prediction of urban built-up area of Hefei city as an example, building a factor system that affects built-up area from the economic, social and environmental dimensions. Then, the paper establishes a quantitative prediction model based on the Radial Basis Function neural network. As a comparison, the paper also uses the Back Propagation neural network to predict. The results show that the Radial Basis Function neural network prediction has a higher accuracy and the prediction result is more reasonable and reliable.

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