Robots moving in formation by using neural network and radial basis functions

2002 ◽  
Author(s):  
K. Hirota ◽  
T. Kuwabara ◽  
K. Ishida ◽  
A. Miyanohara ◽  
H. Ohdachi ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Radial Basis

scholarly journals Three-Dimensional Swirl Flow Velocity-Field Reconstruction Using a Neural Network With Radial Basis Functions

2001 ◽  
Vol 123 (4) ◽  
pp. 920-927 ◽  
Author(s):  
J. Pruvost ◽  
J. Legrand ◽  
P. Legentilhomme
Keyword(s):  
Neural Network ◽  
Velocity Field ◽  
Radial Basis Functions ◽  
Swirling Flow ◽  
Interpolation Method ◽  
Measurement Techniques ◽  
Three Dimensional ◽  
Basis Functions ◽  
Hydrodynamic Characteristics ◽  
Radial Basis

For many studies, knowledge of continuous evolution of hydrodynamic characteristics is useful but generally measurement techniques provide only discrete information. In the case of complex flows, usual numerical interpolating methods appear to be not adapted, as for the free decaying swirling flow presented in this study. The three-dimensional motion involved induces a spatial dependent velocity-field. Thus, the interpolating method has to be three-dimensional and to take into account possible flow nonlinearity, making common methods unsuitable. A different interpolation method is thus proposed, based on a neural network algorithm with Radial Basis Functions.

A SHORT-TERM TEMPERATURE FORECASTER BASED ON A NOVEL RADIAL BASIS FUNCTIONS NEURAL NETWORK

2001 ◽  
Vol 11 (01) ◽  
pp. 71-77 ◽  
Author(s):  
PEDRO A. GONZALEZ LANZA ◽  
JESUS M. ZAMARREÑO COSME
Keyword(s):  
Neural Network ◽  
Radial Basis Functions ◽  
Load Forecasting ◽  
Regression Tree ◽  
Basis Functions ◽  
Short Term ◽  
Rbf Network ◽  
Radial Basis ◽  
Temperature Forecasting ◽  
Require Temperature

Many applications dealing with electric load forecasting in buildings require temperature prediction. A new method for short-term temperature forecasting based on a Radial Basis Functions Neural Network, initialized by a Regression Tree, is presented. In this method, each terminal node of the tree contributes one hidden unit to the RBF network. The forecaster uses the current coded hour and the temperature as inputs, and predicts the next hour temperature. The results demonstrate this predictor can be used for load forecasting.

Analysis of the Effect of Hidden Layer Units on Coronary Heart Prediction Using the Radial Basis Functions Algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 297
Author(s):  
I Gede Bagus Semara Wijaya ◽  
Luh Gede Astuti
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Radial Basis Functions ◽  
Developed Countries ◽  
The United States ◽  
Basis Functions ◽  
The Neural Network ◽  
Radial Basis ◽  
Hidden Layer

Heart disease is a disease that occurs due to disturbances in the heart, especially when pumping blood so that it can cause death. Nearly half of deaths in the United States and other developed countries are caused by heart disease. Therefore, an early prognosis of heart disease is needed to prevent the risk of coronary heart disease. One thing that can be done is to predict coronary heart disease sufferers using the neural network method. This study conducted an analysis of the effect of hidden layer units on the neural network radial basis functions algorithm to predict coronary heart disease sufferers. This study obtained the highest accuracy at 10 hidden layers, namely 85.08%.

Neural-sliding mode approach-based adaptive estimation, isolation and tolerance of aircraft sensor fault

2019 ◽  
Vol 92 (2) ◽  
pp. 237-255 ◽  
Author(s):  
Muhammad Taimoor ◽  
Li Aijun
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Radial Basis Functions ◽  
Sliding Mode ◽  
Basis Functions ◽  
Content Type ◽  
Radial Basis ◽  
The Stability

Purpose The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system. Design/methodology/approach In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft. Findings The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied. Practical implications For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.

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