Hybrid artificial genetic – neural network model to predict the transmission of vibration to the head during whole-body vibration training

In this work, Artificial Neural Network (ANN) modelling has been employed to investigate the effects of various factors on the biodynamic responses to vibration represented by the transmissibility and its phase. These factors include, height, weight, Body Mass Index (BMI), age, frequency and posture. Nine subjects stood on a vibrating plate and were exposed to vertical vibration at nine frequencies in the range 17-46 Hz while adopting four different standing postures; Bent Knee posture (BK), Locked Knee posture (LK), right foot to the Front and left foot to the Back posture (FB) and One Leg posture (OL). The accelerations of the vibrating plate and the head of the subjects were measured during the exposure to vibration in order to calculate the transmissibility between the vibrating plate and the head. Genetic Algorithm (GA) was used to choose ANN’s number of hidden layers and number of neurons in each layer to obtain the best performance for predicting the transmissibility. The GA compared the root mean square errors (RMSE) between the ANN outputs and the experimental outputs, and then choose the best results that could be achieved. The number of hidden layers and number of neurons tested in GA vary from one hidden layer to four hidden layers, and from one neuron per layer to one hundred neurons per layer. Several runs have been conducted to train and validate the ANN model. The results show that double hidden layer with 13 neurons in the first layer and 12 neurons in the second layer give the best candidate. The proposed model can be integrated with whole-body vibration machines in order to choose the suitable exposure based on the user’s characteristics

Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning

Deploying machine learning to improve medical diagnosis is a promising area. The purpose of this study is to identify and analyze unique genetic signatures for breast cancer grades using publicly available gene expression microarray data. The classification of cancer types is based on unsupervised feature learning. Unsupervised clustering use matrix algebra based on similarity measures which made it suitable for analyzing gene expression. The main advantage of the proposed approach is the ability to use gene expression data from different grades of breast cancer to generate features that automatically identify and enhance the cancer diagnosis. In this paper, we tested different similarity measures in order to find the best way that identifies the sets of genes with a common function using expression microarray data

Multilayer perceptron-based DFE with lattice structure

The severely distorting channels limit the use of linear equalizers and the use of the nonlinear equalizers then becomes justifiable. Neural-network-based equalizers, especially the multilayer perceptron (MLP)-based equalizers, are computationally efficient alternative to currently used nonlinear filter realizations, e.g., the Volterra type. The drawback of the MLP-based equalizers is, however, their slow rate of convergence, which limit their use in practical systems. In this work, the effect of whitening the input data in a multilayer perceptron-based decision feedback equalizer (DFE) is evaluated. It is shown from computer simulations that whitening the received data employing adaptive lattice channel equalization algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE. The adaptive lattice algorithm is a modification to the one developed by Ling and Proakis (1985). The consistency in performance is observed in both time-invariant and time-varying channels. Finally, it is found in this work that, for time-invariant channels, the MLP DFE outperforms the least mean squares (LMS)-based DFE. However, for time-varying channels comparable performance is obtained for the two configuration

Control of nonlinear dynamical systems using genetic algorithms

This paper introduces a new method for the control of nonlinear systems using genetic algorithms. The proposed method formulates the nonlinear controller design as an optimization problem and genetic algorithms are used in the optimization process. Simulation examples are included to illustrate the performance and effectiveness of the proposed metho

A compound near-far end least square-fourth error minimization foradaptive echo cancellation

This article presents a novel algorithm for echo cancellers with near-end and far-end sections. The algorithm consists of simultaneously applying the least mean square (LMS) algorithm to the near-end section of the echo canceller and the least mean fourth (LMF) algorithm to the far-end section. This combination results in a substantial improvement of the performance of the proposed scheme over the LMS algorithm in Gaussian and non-Gaussian environments (additive noise). However, the application of the LMF and the LMS algorithms to the near-end and the far-end sections, respectively, results in a poor performance. Simulation results, confirm the superior performance of the new algorith

Multilayer perceptron-based DFE with lattice structure

The severely distorting channels limit the use of linear equalizers and the use of the nonlinear equalizers then becomes justifiable. Neural-network-based equalizers, especially the multilayer perceptron (MLP)-based equalizers, are computationally efficient alternative to currently used nonlinear filter realizations, e.g., the Volterra type. The drawback of the MLP-based equalizers is, however, their slow rate of convergence, which limit their use in practical systems. In this work, the effect of whitening the input data in a multilayer perceptron-based decision feedback equalizer (DFE) is evaluated. It is shown from computer simulations that whitening the received data employing adaptive lattice channel equalization algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE. The adaptive lattice algorithm is a modification to the one developed by Ling and Proakis (1985). The consistency in performance is observed in both time-invariant and time-varying channels. Finally, it is found in this work that, for time-invariant channels, the MLP DFE outperforms the least mean squares (LMS)-based DFE. However, for time-varying channels comparable performance is obtained for the two configuration

Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization

Stop-and-go decision-directed (S&G-DD) equalization is the most primitive blind equalization (BE) method for the cancelling of intersymbol-interference in data communication systems. Recently, this scheme has been applied to complex-valued multilayer feedforward neural network, giving robust results with a lower mean-square error at the expense of slow convergence. To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. Simulation results show the effectiveness of the proposed algorithm in terms of initial convergence

A compound near-far end least square-fourth error minimization foradaptive echo cancellation

This article presents a novel algorithm for echo cancellers with near-end and far-end sections. The algorithm consists of simultaneously applying the least mean square (LMS) algorithm to the near-end section of the echo canceller and the least mean fourth (LMF) algorithm to the far-end section. This combination results in a substantial improvement of the performance of the proposed scheme over the LMS algorithm in Gaussian and non-Gaussian environments (additive noise). However, the application of the LMF and the LMS algorithms to the near-end and the far-end sections, respectively, results in a poor performance. Simulation results, confirm the superior performance of the new algorith

Neural network-based decision feedback equaliser with latticestructure

The effect of whitening the input data in a multilayer perceptron-based decision feedback equaliser (DFE) is evaluated. It is shown from computer simulations that whitening of the received data employing adaptive lattice channel equalisation algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE

Neural network-based decision feedback equaliser with latticestructure

The effect of whitening the input data in a multilayer perceptron-based decision feedback equaliser (DFE) is evaluated. It is shown from computer simulations that whitening of the received data employing adaptive lattice channel equalisation algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE