(LPNN) is employed to select the model parameters of fixed basis signals,and annealed linear programming neural network(ALPNN) is proposed to estimate the model parameters of parametric dependent basis signals.The utility of the neural network approach is illustrated by two simulation results which ...
Optimal operation configuration of a Wireless Sensor Network (WSN) can be determined by utilizing exact mathematical programming techniques such as Mixed I
Neural network Yes No Neural assembly/ensemble Yes Yes The neural network framework has been successfully applied to study cognitive operations, including motor control [6], motor learning [10], working memory [11–17], timing estimation [15,16,18,19], and decision-making [20,21]. The excit...
An artificial neural network (ANN) is a trainable algorithm that can learn to produce an output appropriate for a given input. Such networks can be applied in a wide variety of pattern recognition tasks, including parameter estimation. The major advantages of using ANNs for parameter estimation ar...
parameters are direct indicators of the organization of the underlying phenomenon, the second is the training of an artificial neural network for data smoothing and complementation, and the third is a technique for reinterpreting differential equations in a fashion that facilitates parameter estimation. ...
options = trainingOptions(solverName) returns training options for the optimizer specified by solverName. To train a neural network, use the training options as an input argument to the trainnet function. options = trainingOptions(solverName,Name=Value) returns training options with additional options...
This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown parameters in a “mean value” model, and anticipate maintenance requirements. The PINN model is applied to diesel engines...
Neural-network based predictions of event properties in astro-particle physics are getting more and more common. However, in many cases the result is just
To demonstrate the advancement of the proposed PINN, Multi-Layer Perceptron (MLP) with the same structure and parameter amounts and Convolutional Neural Network (CNN) with similar parameter amounts are used as comparison methods. The details of MLP and CNN can be found in Supplementary Note3. ...
[23] where the authors introduced an artificial neural network based model for kinetic rate parameter estimation during transcytosis of polymeric nanoparticle through the blood-brain barrier. Unlike the traditional fully connected neural network, which can predict the solution of a partial differential ...