The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

This research focuses on the synthesis of carbon nanotube (CNT) and Poly(3-hexylthiophene) (P3HT) (pristine polymer) with Ag doped (CNT/ P3HT@Ag) nanocomposite thin films to be utilised in various practical applications. First, four samples of CNT solution and different ratios of the polymer (P3HT) [0.1, 0.3, 0.5, and 0.7 wt.%] are prepared to form thin layer of P3HT@CNT nanocomposites by dip-coating method of Ag. To investigate the absorption and conductivity properties for use in various practical applications, structure, morphology, optical, and photoluminescence properties of CNT/P3HT @Ag nanocomposite are systematically evaluated in this study. In this regard, the UV/Vis/NIR spectrophotometer in the wavelength range of 350 to 7

Here, we found an estimation of best approximation of unbounded functions which satisfied weighted Lipschitz condition with respect to convex polynomial by means of weighted Totik-Ditzian modulus of continuity

A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.

Research in consumer science has proven that grocery shopping is a complex and distressing process. Further, the task of generating the grocery lists for the grocery shopping is always undervalued as the effort and time took to create and manage the grocery lists are unseen and unrecognized. Even though grocery lists represent consumers’ purchase intention, research pertaining the grocery lists does not get much attention from researchers; therefore, limited studies about the topic are found in the literature. Hence, this study aims at bridging the gap by designing and developing a mobile app (application) for creating and managing grocery lists using modern smartphones. Smartphones are pervasive and become a necessity for everyone tod

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is appl

Complexes ofCo(ll),Cu(||),Ni(||),pt(|| ),and pd(||) with N3O-chelating Ligand Incorporating Azo and Shiff Base Moieties ;synthesis, spectroscopic ,Thermal Decomposition Theoretical