This article presents two systems that have the ability to simulate and predict the proton-proton (P-P) interaction. They are an adaptive neurofuzzy inference system (ANFIS) and a neural networks (NNs) system. The P-P-based ANFIS and NNs models calculate the multiplicity distribution of charged particles at different high energies. Simulation results of training charged particles using the ANFIS and NNs as tested with training data points showed perfect fitting to the experimental data. Prediction capabilities of the ANFIS and NNs checked with data points not used in training also proved to perform well. The results amply demonstrate the feasibility of these techniques in extracting the collision features and prove their effectiveness. It is found that the ANFIS shows better performance and trained more quickly than the NNs system.

A set of algorithms for computing the fractal dimension from image data is introduced. The advantages and disadvantages for each algorithm are clarified. The implementation of some of these algorithms for evaluating the fractal dimension to several images is also given. Such images are considered in the present work to be generated with the Mathematica package using fractal Brownian motion. Moreover, the author generates other images by introducing a noise with different levels to a smooth gray level surface. As a result, it is concluded that the Sarkar method gives results that are more efficient and accurate than the other considered results. Our conclusion is supported by charts illustrating these results.

This paper presents an automatic system of neural networks (NNs) that has the ability to simulate and predict many of applied problems. The system architectures are automatically reorganized and the experimental process starts again, if the required performance is not reached. This processing is continued until the performance obtained. This system is first applied and tested on the two spiral problem; it shows that excellent generalization performance obtained by classifying all points of the two-spirals correctly. After that, it is applied and tested on the shear stress and the pressure drop problem across the short orifice die as a function of shear rate at different mean pressures for linear low-density polyethylene copolymer (LLDPE) at190∘C. The system shows a better agreement with an experimental data of the two cases: shear stress and pressure drop. The proposed system has been also designed to simulate other distributions not presented in the training set (predicted) and matched them effectively.