In this paper, we propose an optimized method for nonlinear function approximation based on multiplierless piecewise linear computation (ML-PLAC), which call OML-PLAC. OML-PLAC finds the minimum number of segments with predefined fractional bit width input/output, maximum shift-and-add operations, user-defined widths intermediate data, and absolute error (MAE). addition, minimizes actual MAE as...

In this paper, using the Steklov function, we introduce the generalized continuity modulus and denethe class of functions Wr;kp;' in the space Lp. For this class, we prove an analog of the estimates in [1]in the space Lp.

In this article, we apply the Multiquadric radial basis function (RBF) interpo-lation method for nding the numerical approximation of traveling wave solu-tions of the Kawahara equation. The scheme is based on the Crank-Nicolsonformulation for space derivative. The performance of the method is shown innumerical examples.

Relational reinforcement learning combines traditional reinforcement learning with a strong emphasis on a relational (rather than attribute-value) representation. Earlier work used relational reinforcement learning on a learning version of the classic Blocks World planning problem (a version where the learner does not know what the result of taking an action will be). “Structural” learning resu...

Automated agents designed to learn strategies using Markov decision processes on a continuous state space usually need to approximate the value function associated with the environment. Traditionally, as described in Sutton and Barto in [1], this is often done using a fixed number of rectangular features tiled across the state space, possibly distributed into multiple layers. As an improvement ...

In this paper, seventh-order iterative methods for the solution ofnonlinear equations are presented. The new iterative methods are developed byusing weight function method and using an approximation for the last derivative,which reduces the required number of functional evaluations per step. Severalexamples are given to illustrate the eciency and the performance of the newiterative methods.