Novel Inset Feed Circular Slotted Microstrip Antenna Using Multilayer Feed-Forward Back-Propagation and Radial Basis Function Neural Network

2020 ◽  
Vol 43 (4) ◽  
pp. 343-345
Author(s):  
Vinod Kumar Singh ◽  
Archana Lala ◽  
Ashutosh Kumar Singh
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Microstrip Antenna ◽  
Back Propagation ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Radial Basis

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.

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 The choice between Radial Basis function and Feed Forward Neural Network to predict long term tidal condition

2020 ◽  
Vol 4 (1) ◽  
pp. 1-9
Author(s):  
Hengameh Motamedi ◽  
◽  
Maryam Rahbani ◽  
Abbas Harifi ◽  
Danial Ghaderi ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Radial Basis ◽  
Tidal Condition

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.

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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