Application of neural networks to inverse lens distortion modelling

Improved neural network modeling of inverse lens distortion

Inverse lens distortion modelling allows one to find the pixel in a distorted image which corresponds to a known point in object space, such as may be produced by RADAR. This paper extends recent work using neural networks as a compromise between processing complexity, memory usage and accuracy. The already encouraging results are further enhanced by considering different neuron activation func...

GMDH Type Neural Networks and Their Application to the Identification of the Inverse Kinematics Equations of Robotic Manipulators (RESEARCH NOTE)

Prediction of Deformation of Circular Plates Subjected to Impulsive Loading Using GMDH-type Neural Network

In this paper, experimental responses of the clamped mild steel, copper, and aluminium circular plates are presented subjected to blast loading. The GMDH-type neural networks (Group Method of Data Handling) are then used for the modelling of the mid-point deflection thickness ratio of the circular plates using those experimental results. The aim of such modelling is to show how the mid-point de...

Application of artificial neural network and genetic algorithm to modelling the groundwater inflow to an advancing open pit mine

In this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. Novel hybrid method coupling artificial neural network (ANN) with genetic algorithm (GA) called ANN-GA, was utilised. Ratios of pit depth to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the hydraulic head (HH) in the observation w...

APPLICATION NEURAL NETWORK TO SOLVE ORDINARY DIFFERENTIAL EQUATIONS

In this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. In proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. In comparison with existing similar neural networks proposed model provides solutions with high accuracy. Numerica...