Annealed f-Smoothing as a Mechanism to Speed up Neural Network Training

A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran

Numerical and Neural Network Modeling and control of an Aircraft Propeller

In this paper, parametric and numerical model of the DC motor, connected to aircraft propellers are extracted. This model is required for controlling trust and velocity of the propellers, and consequently, an aircraft. As a result, both of torque and speed of the propeller can be controlled simultaneously which increases the kinematic and kinetic performance of the aircraft. Parametric model of...

A conjugate gradient based method for Decision Neural Network training

Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...

A Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network

Abstract   Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...

A hierarchical Convolutional Neural Network for Segmentation of Stroke Lesion in 3D Brain MRI

Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, a...