In the present paper, informatics-aided quantitative structure activity relationship (QSAR) models using genetic algorithm-partial least square (GA-PLS), genetic algorithm-Kernel partial least square (KPLS), and Levenberg-Marquardt artificial neural network (LM ANN) approach were constructed to access the antimalarial activity (pIC50) of 2,5-diaminobenzophenone derivatives. Comparison of errors...

In this thesis, a special class of Recurrent Neural Networks (RNN) is employed for system identification and predictive control of time dependent systems. Fundamental architectures and learning algorithms of RNNs are studied upon which a generalized architecture over a class of state-space represented networks is proposed and formulated. Levenberg-Marquardt (LM) learning algorithm is derived fo...

In this paper, we adopt the Levenberg-Marquardt (LM) algorithm to implement the nonlinear multivariable optimization for azimuth/elevation angle-of-arrival (AOA) estimation based on the Capon beamforming algorithm. The formulation is based on the fact that the cost function of the Capon algorithm can be expressed in a least-squares form. The performance in terms of the root mean square error (R...

The channel estimation algorithms play a vital role in third generation (3G) communication systems to support efficient spectrum utilization. The transition from 3G to 4G systems is to provide high data rate, error free low complexity system with efficient adaptive techniques. A noisy Channel estimation and feedback error introduces imperfect Channel State Information (CSI). This paper introduc...

Introduction: Energy resolving X-ray photon counting detectors are able to assign each detected photon to energy bins [1] (Figure 1). This allows the decomposition of an X-ray image into the materials of the acquired object [1, 2], which has potential benefits in angiography. Contrast agent can be extracted without acquiring a mask image, possibly saving dose and avoiding problems with patient ...

In this work, two modifications on Levenberg-Marquardt algorithm for feedforward neural networks are studied. One modification is made on performance index, while the other one is on calculating gradient information. The modified algorithm gives a better convergence rate compared to the standard Levenberg-Marquard (LM) method and is less computationally intensive and requires less memory. The p...

In this paper, Levenberg-Marquardt (LM) learning algorithm for a single Integrate-and-Fire Neuron (IFN) is proposed and tested for various applications in which a neural network based on multilayer perceptron is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Sever...

This chapter focuses on comparing the forecasting ability of the backpropagation neural network (BPNN) and the nonlinear autoregressive moving average with exogenous inputs (NARX) network trained with different algorithms; namely the quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), conjugate gradient (Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart), and Levenberg-Marqu...

objective: in this study, artificial neural network (ann) analysis of virotherapy in preclinical breast cancer was investigated. materials and methods: in this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. the input parameters of the model were virus dose, week and tamoxifen ci...

Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a ...