Single-Layer Functional Link Artificial Neural Network with Regression-Based Weights

2017 ◽  
pp. 77-86
Author(s):  
Snehashish Chakraverty ◽  
Susmita Mall
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Single Layer ◽  
Functional Link ◽  
Artificial Neural

scholarly journals A Single Layer Functional Link Artificial Neural Network based on Chebyshev Polynomials for Neural Evaluations of Nonlinear Nth Order Fuzzy Differential Equations

2018 ◽  
Vol 56 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Asmat Ara ◽  
Oyoon Abdul Razzaq ◽  
Najeeb Alam Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Equations ◽  
Single Layer ◽  
Science And Engineering ◽  
Functional Link ◽  
Chebyshev Neural Network ◽  
Runge Kutta Method ◽  
Artificial Neural ◽  
Generalized Hukuhara Differentiability

Abstract Bearing in mind the considerable importance of fuzzy differential equations (FDEs) in different fields of science and engineering, in this paper, nonlinear nth order FDEs are approximated, heuristically. The analysis is carried out on using Chebyshev neural network (ChNN), which is a type of single layer functional link artificial neural network (FLANN). Besides, explication of generalized Hukuhara differentiability (gH-differentiability) is also added for the nth order differentiability of fuzzy-valued functions. Moreover, general formulation of the structure of ChNN for the governing problem is described and assessed on some examples of nonlinear FDEs. In addition, comparison analysis of the proposed method with Runge-Kutta method is added and also portrayed the error bars that clarify the feasibility of attained solutions and validity of the method.

ARTIFICIAL NEURAL NETWORK BASED SOFT ESTIMATOR FOR ESTIMATION OF TRANSDUCER STATIC NONLINEARITY

2004 ◽  
Vol 14 (04) ◽  
pp. 237-246 ◽  
Author(s):  
AMAR PARTAP SINGH ◽  
TARA SINGH KAMAL ◽  
SHAKTI KUMAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Single Layer ◽  
Functional Link ◽  
Static Nonlinearity ◽  
Feed Forward Back Propagation ◽  
Direct Model ◽  
Artificial Neural

In this work, the development of an Artificial Neural Network (ANN) based soft estimator is reported for the estimation of static-nonlinearity associated with the transducers. Under the realm of ANN based transducer modeling, only two neural models have been suggested to estimate the static-nonlinearity associated with the transducers with quite successful results. The first existing model is based on the concept of a functional link artificial neural network (FLANN) trained with μ-LMS (Least Mean Squares) learning algorithm. The second one is based on the architecture of a single layer linear ANN trained with α-LMS learning algorithm. However, both these models suffer from the problem of slow convergence (learning). In order to circumvent this problem, it is proposed to synthesize the direct model of transducers using the concept of a Polynomial-ANN (polynomial artificial neural network) trained with Levenberg-Marquardt (LM) learning algorithm. The proposed Polynomial-ANN oriented transducer model is implemented based on the topology of a single-layer feed-forward back-propagation-ANN. The proposed neural modeling technique provided an extremely fast convergence speed with increased accuracy for the estimation of transducer static nonlinearity. The results of convergence are very stimulating with the LM learning algorithm.

scholarly journals A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

2021 ◽  
Author(s):  
Kathakali Sarkar ◽  
Deepro Bonnerjee ◽  
Rajkamal Srivastava ◽  
Sangram Bagh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Basic Concept ◽  
Single Layer ◽  
Artificial Neural ◽  
Engineered Bacteria ◽  
Network Type

Here, we adapted the basic concept of artificial neural networks (ANN) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible...

Firefly Algorithm Optimized Functional Link Artificial Neural Network for ISA-Radar Image Recognition

2021 ◽  
Author(s):  
Asma Elyounsi ◽  
Hatem Tlijani ◽  
Mohamed Salim Bouhlel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Problem Size ◽  
Functional Link ◽  
Optimal Weights ◽  
Feed Forward Network ◽  
Higher Order Neural Networks ◽  
Artificial Neural

Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods.

scholarly journals Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

2009 ◽  
Vol 16 (3) ◽  
pp. 325-334 ◽  
Author(s):  
Ya-li Zhou ◽  
Qi-zhi Zhang ◽  
Tao Zhang ◽  
Xiao-dong Li ◽  
Woon-seng Gan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Noise Control ◽  
Active Noise Control ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Functional Link ◽  
Active Noise ◽  
Artificial Neural

In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

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