This paper investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a 3-dimensional, artificial quadruped creature simulated in a physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate a pareto optimal set of artificial neural networks that optimizes the conf...

The application of pattern recognition (PR) techniques, artificial neural networks (ANNs), and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The fundamental aim of the paper is to outline the importance of soft computing and hybrid AI techniques in manufacturing by introducing a genetic algo...

Artificial Neural Networks (ANNs) have been applied to predict many complex problems. In this paper ANNs are applied to horse racing prediction. We employed Back-Propagation, Back-Propagation with Momentum, QuasiNewton, Levenberg-Marquardt and Conjugate Gradient Descent learning algorithms for real horse racing data and the performances of five supervised NN algorithms were analyzed. Data colle...

In the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised backpropagation learning algorithm are...

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of...

conclusions results showed that ann is a powerful tool for predicting sorption coefficients using soil organic carbon content variations. results the multilayer perceptron (mlp) artificial neural networks (ann) model with 1-6-1 layout, predicted nearly 98% of the variance of kd as well as 94% of the koc variations with soil organic carbon content. materials and methods data of this study were d...

Artificial neural networks (ANNs) are widely used to model low-level neural activities and high-level cognitive functions. In this article, we review the applications of statistical inference for learning in ANNs. Statistical inference provides an objective way to derive learning algorithms both for training and for evaluation of the performance of trained ANNs. Solutions to the over-fitting pr...

To hear more natural synthetic speech generated by a Korean TTS (Text-To-Speech) system, we have to know all the possible prosodic rules in Korean language. We can extract these rules from linguistic, phonetic knowledge or by analyzing real speech. In general, all of these rules are integrated into a prosody-generation algorithm in TTS. But this algorithm cannot cover all the possible prosodic ...

Evaluating the performance of artificial neural networks (ANNs) for predicting physical, rheological, and colorimetric properties chitosan nanoparticles (CSNPs)

abstract infiltration is a significant process which controls the fate of water in the hydrologic cycle. the direct measurement of infiltration is time consuming, expensive and often impractical because of the large spatial and temporal variability. artificial neural networks (anns) are used as an indirect method to predict the hydrological processes. the objective of this study was to develop ...