Mobile Phone Usage Detection by ANN Trained with a Metaheuristic Algorithm †

Abstract

Artificial neural networks (ANN) are widely used to classify high non-linear systems by using a set of input/output data. Moreover, they are trained using several optimization methodologies and this paper presents a novel algorithm for training ANN through an earthquake optimization method. Usually, gradient optimization method is implemented for the training process, with perhaps the large number of iterations leading to slow convergence, and not always achieving the optimal solution. Since metaheuristic optimization methods deal with searching for weight values in a broad optimization space, the training computational effort is reduced and ensures an optimal solution. This work shows an efficient training process that is a suitable solution for detection of mobile phone usage while driving. The main advantage of training ANN using the Earthquake Algorithm (EA) lies in its versatility to search in a fine or aggressive way, which extends its field of application. Additionally, a basic example of a linear classification is illustrated using the proposal-training method, so the number of applications could be expanded to nano-sensors, such as reversible logic circuit synthesis in which a genetic algorithm had been implemented. The fine search is important for the studied logic gate emulation due to the small searching areas for the linear separation, also demonstrating the convergence capabilities of the algorithm. Experimental results validate the proposed method for smart mobile phone applications that also can be applied for optimization applications.

Hybrid chaotic whale-shark optimization algorithm to improve artificial neural network: application to the skin neglected tropical diseases diagnosis

“…The earthquake optimisation (EA) algorithm was first introduced by Mendez et al in [95] as a method of tuning a PID controller. It was then validated as a method for training an ANN in [95] through data acquired from GPS sensors and accelerometers of mobile phones, achieving a very high efficiency rate. Like other MHAs, the performance of the EA depends on the first population (initialization).…”

Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm

“…The Earthquake Algorithm (EA) is the first geo-inspired metaheuristic algorithm (as explained in [14]), based on the behavior of the P and S waves existing in earthquakes. The first sketch of the algorithm appeared in [32], while the first complete version of the algorithm was formally introduced in [33], with its extended version in [13].…”

“…Therefore, as explained in [13], the algorithm takes advantage of the characteristics between wave velocities, where the P-wave is faster on the earth material compressibility, meanwhile the S-wave is slower and directly depends on rocks elasticity causing epicenters to move up and down, constantly perpendicular to the wave direction. For that reason (also from [13]), Equations ( 9) and (10), are the given mathematical expressions for the P and S waves velocities respectively.…”

“…Accordingly, this work presents a novel alternative for MPPT, based on the Earthquake Optimization Algorithm (EA), which enables an optimization with an easy parameters calibration and an improved dynamic behavior. The proposed algorithm is based on the main EA explained in [13], where the metaheuristic algorithm is characterized for using two different velocity equations, nature-inspired by the propagation equations of the P and S waves, which are part of physical earthquakes.…”

ANN Based MRAC-PID Controller Implementation for a Furuta Pendulum System Stabilization

“…On the other hand, the main objective of designed ANN in this work, is to emulate the behavior of the MRAC-PID controller implemented in [11], extending the operating margins and maintaining the stability gained by the original MRAC-PID, but with lower computational effort; which is indeed, a common characteristic of the implementation of ANN's (as discussed in [13] and [10]).…”