scholarly journals Three-Dimensional Force Prediction of a Flexible Tactile Sensor Based on Radial Basis Function Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Feilu Wang ◽  
Yang Song
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Nonlinear Approximation ◽  
Three Dimensional ◽  
Sliding Window ◽  
Tactile Sensor ◽  
Least Square ◽  
Force Prediction ◽  
Conductive Rubber ◽  
Radial Basis

A flexible tactile sensor array with 6 × 6 N-type sensitive elements made of conductive rubber is presented in this paper. The property and principle of the tactile sensor are analyzed in detail. Based on the piezoresistivity of conductive rubber, this paper takes full advantage of the nonlinear approximation ability of the radial basis function neural network (RBFNN) method to approach the high-dimensional mapping relation between the resistance values of the N-type sensitive element and the three-dimensional (3D) force and to accomplish the accurate prediction of the magnitude of 3D force loaded on the sensor. In the prediction process, the k -means algorithm and recursive least square (RLS) method are used to optimize the RBFNN, and the k -fold cross-validation method is conducted to build the training set and testing set to improve the prediction precision of the 3D force. The optimized RBFNN with different spreads is used to verify its influence on the performance of 3D force prediction, and the results indicate that the spread value plays a very important role in the prediction process. Then, sliding window technology is introduced to build the RBFNN model. Experimental results show that setting the size of the sliding window appropriately can effectively reduce the prediction error of the 3D force exerted on the sensor and improve the performance of the RBFNN predictor, which means that the sliding window technology is very feasible and valid in 3D force prediction for the flexible tactile sensor. All of the results indicate that the optimized RBFNN with high robustness can be well applied to the 3D force prediction research of the flexible tactile sensor.

scholarly journals An Improved Radial Basis Function Networks Based on Quantum Evolutionary Algorithm for Training Nonlinear Datasets

2019 ◽  
Vol 8 (2) ◽  
pp. 120
Author(s):  
Lim Eng Aik ◽  
Tan Wei Hong ◽  
Ahmad Kadri Junoh
Keyword(s):  
Quantum Computing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Nonlinear Approximation ◽  
Radial Basis Function Network ◽  
Nonlinear Function ◽  
Oxygen Demand ◽  
Experimental Models ◽  
Air Pollutant ◽  
Radial Basis

In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread value. Radial basis function network (RBFN) is a type of feedforward network that capable of perform nonlinear approximation on unknown dataset. It has been widely used in classification, pattern recognition, nonlinear control and image processing. Thus, with the increases in RBFN application, some problems and weakness of RBFN network is identified. Through the combination of quantum computing and RBFN provides a new research idea in design and performance improvement of RBFN system. This paper describes the theory and application of quantum computing and cloning operators, and discusses the superiority of these theories and the feasibility of their optimization algorithms. This proposed improved RBFN (I-RBFN) that combined with cloning operator and quantum computing algorithm demonstrated its ability in global search and local optimization to effectively speed up learning and provides better accuracy in prediction results. Both the algorithms that combined with RBFN optimize the centers and spread value of RBFN. The proposed I-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to I-RBFN for root mean square error (RMSE) values with standard RBFN. The proposed I-RBFN yielded better results with an average improvement percentage more than 90 percent in RMSE.

Three Dimensional Analysis of SPECT Images for Diagnosing Early Parkinson’s Disease using Radial Basis Function Kernel − Extreme Learning Machine

2019 ◽  
Vol 15 (5) ◽  
pp. 461-470
Author(s):  
Sebasthiyar Anita ◽  
Panchnathan Aruna Priya
Keyword(s):  
Parkinson’S Disease ◽  
Radial Basis Function ◽  
Extreme Learning Machine ◽  
Basis Function ◽  
Three Dimensional ◽  
Threshold Level ◽  
Radial Basis Function Kernel ◽  
Radial Basis ◽  
Specific Uptake ◽  
Learning Machine

Background: Parkinson’s Disease (PD) is caused by the deficiency of dopamine, the neurotransmitter that has an effect on specific uptake region of the substantia nigra. Identification of PD is quite tough at an early stage. Objective: The present work proposes an expert system for three dimensional Single-Photon Emission Computed Tomography (SPECT) image to diagnose the early PD. Methods: The transaxial image slices are selected on the basis of their high specific uptake region. The processing techniques like preprocessing, segmentation and feature extraction are implemented to extract the quantification parameters like Intensity, correlation, entropy, skewness and kurtosis of the images. The Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers using Radial Basis Function kernel (RBF) are implemented and their results are compared in order to achieve better performance of the system. The performance of the system is evaluated in terms of sensitivity, specificity analysis, accuracy, Receiver Operating Curve (ROC) and Area Under the Curve (AUC). Results: It is found that RBF-ELM provides high accuracy of 98.2% in diagnosing early PD. In addition, the similarity among the features is found out using K-means clustering algorithm to compute the threshold level for early PD. The computed threshold level is validated using Analysis of Variance (ANOVA). Conclusion: The proposed system has a great potential to assist the clinicians in the early diagnosis process of PD.

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