comparison of mlp and rbf neural networks performance for estimation of broiler output energy

comparison of zoe and vitapex for canal treatment of necrotic primary teeth

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Developing an Artificial Neural Networks Model for Predicting Output Energy and GHG Emission of Strawberry Production

Comparative Study of MLP and RBF Neural Networks for Estimation of Suspended Sediments in Pari River, Perak

Estimation of suspended sediments in rivers using soft computing techniques has been extensively performed around the world since 1990’s. However, accuracy in the results was always found to be highly desired and a profound crucial task. This study presents a thorough comparison between the performances of best basis function of Radial Basis Functions (RBF) and the best training algorithm in Mu...

Application of Two Methods of Artificial Neural Network MLP, RBF for Estimation of Wind of Sediments (Case Study: Korsya of Darab Plain)

The lack of sediment gauging stations in the process of wind erosion, caused of estimate of sediment be process of necessary and important. Artificial neural networks can be used as an efficient and effective of tool to estimate and simulate sediments. In this paper two model multi-layer perceptron neural networks and radial neural network was used to estimate the amount of sediment in Korsya o...

a comparison of linguistic and pragmatic knowledge: a case of iranian learners of english

در این تحقیق دانش زبانشناسی و کاربردشناسی زبان آموزان ایرانی در سطح بالای متوسط مقایسه شد. 50 دانش آموز با سابقه آموزشی مشابه از شش آموزشگاه زبان مختلف در دو آزمون دانش زبانشناسی و آزمون دانش گفتار شناسی زبان انگلیسی شرکت کردند که سوالات هر دو تست توسط محقق تهیه شده بود. همچنین در این تحقیق کارایی کتابهای آموزشی زبان در فراهم آوردن درون داد کافی برای زبان آموزان ایرانی به عنوان هدف جانبی تحقیق ...

Output feedback control of nonlinear systems using RBF neural networks

An adaptive output feedback control scheme for the output tracking of a class of continuous-time nonlinear plants is presented. An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method uses parameter projection, control saturation, and a high-gain observer to achieve semi-global uniform ultimat...