Aircraft Parameter Estimation using Feedforward Neural Networks With Lyapunov Stability Analysis

rodbar dam slope stability analysis using neural networks

در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...

Regularization parameter estimation for feedforward neural networks

Under the framework of the Kullback-Leibler (KL) distance, we show that a particular case of Gaussian probability function for feedforward neural networks (NNs) reduces into the first-order Tikhonov regularizer. The smooth parameter in kernel density estimation plays the role of regularization parameter. Under some approximations, an estimation formula is derived for estimating regularization p...

Aircraft Parameter Estimation using Neural Network based Algorithm

Aircraft Parameter estimation is probably the most outstanding and illustrated example of the system identification methodology. In the past the most widely used parameter estimation methods have been Equation error method, Output error method, Maximum likelihood method and Filter error method. In this paper an algorithm based on neural modeling and Gauss-Newton optimization is proposed to esti...

Parameter-estimation-based learning for feedforward neural networks: convergence and robustness analysis

Fast cosmological parameter estimation using neural networks

We present a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological parameter estimation. The algorithm, called COSMONET, is based on training a multilayer perceptron neural network and shares all the advantages of the recently released PICO algorithm of Fendt & Wandelt, but has several additional benefits in terms of ...