Functional link neural network approach to solve structural system identification problems

Sahoo, Deepti Moyi; Chakraverty, Snehashish

doi:10.1007/s00521-017-2907-x

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…The expansion is done using three commonly used functions, i.e., trigonometric, Chebyshev expansion, and Legendre expansion. A traditional FLANN uses trigonometric functions, whereas the other two expansions are based on Legendre [55,56] and Chebyshev [57] polynomials. Ch-4…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

et al. 2022

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Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

et al. 2022

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“…Listed in Table 2 are the required parameters in the Genetic Algorithm implementation. Functional Link Neural Network classification algorithm according to [8] in [11]: Based on Figure 3, the first step is to find the value of The Legendre Polynomial with Equation 6as the base function.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Functional Link Neural Network is an artificial neural network that has a single layer architecture, so that FLNN does not have a hidden layer[5]. Based on[8] from[5] when compared to neural networks that use hidden layers, it can be said that FLNN has more efficient and faster computation when compared to Multilayer Neural Network (MNN). This is supported in[4] which explains in his research that the Legendre Polynomial base function is able to provide the most optimal results compared to other FLNN base function in classifying microarray data.…”

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confidence: 99%

Cancer Detection based on Microarray Data Classification Using FLNN and Hybrid Feature Selection

Afif

Adiwijaya

Astuti

2021

RESTI

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Cancer is one of the second deadliest diseases in the world after heart disease. Citing from the WHO's report on cancer, in 2018 there were around 18.1 million cases of cancer in the world with a total of 9.6 million deaths. Now that bioinformatics technology is growing and based on WHO’s report on cancer, an early detection is needed where bioinformatics technology can be used to diagnose cancer and to help to reduce the number of deaths from cancer by immediately treating the person. Microarray DNA data as one of the bioinformatics technology is becoming popular for use in the analysis and diagnosis of cancer in the medical world. Microarray DNA data has a very large number of genes, so a dimensional reduction method is needed to reduce the use of features for the classification process by selecting the most influential features. After the most influential features are selected, these features are going to be used for the classification and predict whether a person has cancer or not. In this research, hybridization is carried out by combining Information Gain as a filtering method and Genetic Algorithm as a wrapping method to reduce dimensions, and lastly FLNN as a classification method. The test results get colon cancer data to get the highest accuracy value of 90.26%, breast cancer by 85.63%, lung cancer and ovarian cancer by 100%, and prostate cancer by 94.10%.

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“…Periconceptionally, an adaptive neural network is functional to establish models to resolve the feature relationship between different data series [25,26]. e neural network (NN) is a computational model comprised of a large number of connected nodes, each of which performs a simple calculation [27]. It performs well in dealing with the problem of non-normal distribution and is responsible for many of the recent advances in artificial intelligence [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Dual-Network Modeling Architecture for Statistical Evaluation of College Graduates’ Working Ability in Consistence with Their Job Position and Remuneration

Hong

Yang

Wen

et al. 2022

Computational Intelligence and Neuroscience

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Optimal human resources allocation asks to employ a person to work in the position corresponding to his/her ability. Employment competence is the key feedback to the cultivation of college students’ working ability. The data relationship needs to analyze between the in-school cultivation items and the working abilities required by the companies. Machine learning framework is introduced to study the companies’ responses to the cultivation of college students. In this work, a dual-network architecture is built up for statistical modeling evaluation of college graduates’ working ability in consistence with their job position and remuneration. A requirement network and a cultivation network are constructed for extracting features from the original working ability data required by companies and cultivated ever in school. The networks are fully trained by adaptively tuning the linking weights. The extracted features are fused together to estimate the working competence of each target sample/person. To evaluate the dual-network model, a modeling index system is designed, including proposing a total evaluation index calculus for the dual-network model, and a variable importance index from the original data. The samples are consequently ranked by the model predicted index and by the variable importance index, respectively. The ranking difference is used to evaluate the prediction efficiency of the dual-network model. Experimental results show that the dual network architecture is feasible to establish statistical models for the evaluation of college graduates’ in-school cultivated working ability in consistence with the company’s required working ability at their job position and their deserved remuneration.

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Functional link neural network approach to solve structural system identification problems

Cited by 8 publications

References 26 publications

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

Cancer Detection based on Microarray Data Classification Using FLNN and Hybrid Feature Selection

A Dual-Network Modeling Architecture for Statistical Evaluation of College Graduates’ Working Ability in Consistence with Their Job Position and Remuneration

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