Topological sensitivity based far-field detection of elastic inclusions

Abbas, Tasawar; Khan, Shujaat; Sajid, Muhammad Munir; Wahab, Abdul; Ye, Jong Chul

doi:10.1016/j.rinp.2017.12.041

Cited by 7 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the training of proposed Spatio-Temporal RBFNN (STRBF-NN), we employed the widely used least square method [26] called gradient descent algorithm. Consider an RBFNN as shown in Fig.1, the final mapping, at the kth learning iteration at a particular epoch, is given as:…”

Section: A Gradient Descent-based Spatio-temporal Rbfnnmentioning

confidence: 99%

Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

Sadiq

Ibrahim

Usman

et al. 2018

2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)

Self Cite

View full text Add to dashboard Cite

Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extension of RBF neural networks for the prediction of chaotic time series. The proposed algorithm utilizes the concept of time-space orthogonality and separately deals with the temporal dynamics and spatial nonlinearity(complexity) of the chaotic series. The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF. The spatio-temporal RBF is shown to out perform the standard RBFNN by achieving significantly reduced estimation error.

show abstract

Section: A Gradient Descent-based Spatio-temporal Rbfnnmentioning

confidence: 99%

Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

Sadiq

Ibrahim

Usman

et al. 2018

2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the training of proposed statio-temporal RBFNN, we employed the widely used least square method [17] called gradient descent algorithm. Consider an RBFNN as shown in Fig.1, the final mapping, at the kth learning iteration at a particular epoch, is given as:…”

Section: A Gradient Descent-based Spatio-temporal Rbfnnmentioning

confidence: 99%

“…For the training of proposed statio-temporal RBFNN, we employed the widely used least square method [17] called gradient descent algorithm. Consider an RBFNN as shown in Fig.…”

Section: A Gradient Descent-based Spatio-temporal Rbfnnmentioning

confidence: 99%

Spatio-Temporal RBF Neural Networks

Khan

Jafarian²,

Sadiq³

et al. 2018

2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)

Self Cite

View full text Add to dashboard Cite

Herein, we propose a spatio-temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. The proposed RBF architecture is explored for the estimation of a highly nonlinear system and results are compared with the standard architecture for both the conventional and fractional gradient decent-based learning rules. The spatio-temporal RBF is shown to perform better than the standard and fractional RBFNNs by achieving fast convergence and significantly reduced estimation error.

show abstract

“…Due to their simplicity and ease of implementation the Least square-based methods are considered to be widely used optimization techniques for adaptive systems. The technique has been applied in diversified applications such as function approximation [2], detection of elastic inclusions [3], noise cancellation [4], nonlinear system identification [5], ECG signal analysis [6], elasticity imaging [7], and time series prediction [8], etc. Adaptive filters are used to extract the desired components from a signal containing both desired and undesired components.…”

Section: Introductionmentioning

confidence: 99%

q-LMF: Quantum Calculus-Based Least Mean Fourth Algorithm

Sadiq

Usman

Khan

et al. 2019

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

Channel estimation is an essential part of modern communication systems as it enhances the overall performance of the system. In recent past a variety of adaptive learning methods have been designed to enhance the robustness and convergence speed of the learning process. However, the need for an optimal technique is still there. Herein, for non-Gaussian noisy environment we propose a new class of stochastic gradient algorithm for channel identification. The proposed q-least mean fourth (q-LMF) is an extension of least mean fourth (LMF) algorithm and it is based on the q-calculus which is also known as Jackson derivative. The proposed algorithm utilizes a novel concept of error-correlation energy and normalization of signal to ensure high convergence rate, better stability and low steady-state error. Contrary to the conventional LMF, the proposed method has more freedom for large step-sizes. Extensive experiments show significant gain in the performance of the proposed q-LMF algorithm in comparison to the contemporary techniques.

show abstract

Topological sensitivity based far-field detection of elastic inclusions

Cited by 7 publications

References 48 publications

Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

Spatio-Temporal RBF Neural Networks

q-LMF: Quantum Calculus-Based Least Mean Fourth Algorithm

Contact Info

Product

Resources

About