A Neural Network Based Recursive Least Square Multilateration Technique for Indoor Positioning

2021 ◽  
Author(s):  
Bhagawat Adhikari ◽  
Xavier N. Fernando
Keyword(s):  
Neural Network ◽  
Indoor Positioning ◽  
Least Square ◽  
Recursive Least Square

scholarly journals Breast Cancer Detection using Recursive Least Square and Modified Radial Basis Functional Neural Network

2012 ◽  
pp. 57-64
Author(s):  
Manas Rajan Senapati ◽  
P. K. Routray ◽  
Pradipta Kishore Dash
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Back Propagation ◽  
Least Square ◽  
Recursive Least Square ◽  
Data Set ◽  
New Approach ◽  
Radial Basis ◽  
Functional Neural Network ◽  
Hidden Layer

A new approach for classification has been presented in this paper. The proposed technique, Modified Radial Basis Functional Neural Network (MRBFNN) consists of assigning weights between the input layer and the hidden layer of Radial Basis functional Neural Network (RBFNN). The centers of MRBFNN are initialized using Particle swarm Optimization (PSO) and variance and centers are updated using back propagation and both the sets of weights are updated using Recursive Least Square (RLS). Our simulation result is carried out on Wisconsin Breast Cancer (WBC) data set. The results are compared with RBFNN, where the variance and centers are updated using back propagation and weights are updated using Recursive Least Square (RLS) and Kalman Filter. It is found the proposed method provides more accurate result and better classification.

A Spectrum Analysis Method Based on Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2846-2849
Author(s):  
Wei Ping Cui ◽  
Zhi Wen Cao
Keyword(s):  
Neural Network ◽  
Spectrum Analysis ◽  
Amplitude Spectrum ◽  
Complex Multiplication ◽  
Signal Amplitude ◽  
Least Square ◽  
Analysis Method ◽  
Recursive Least Square ◽  
Software And Hardware ◽  
Filtering Function

This paper presents a spectrum analysis method using recursive least square algorithm to train the weights of Fourier Basis Functions (FBF) neural network, according to the weight to obtain the signal amplitude spectrum and phase spectrum. The method does not involve complex multiplication and addition operations, convenient for software and hardware, especially suitable for DSP software and hardware implementation. The simulation results show that, this method is not only high precision, fast calculation speed, but also has the noise filtering function, is a kind of effective method for spectrum analysis.

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