Background: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort NEURALspikes automatically. However, sorting NEURAL spikes is a challenging task because of the low signal to noise ratio (SNR) of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system.Methods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect NEURAL spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a RADIAL BASIS function (RBF) NEURAL network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF NEURAL network was used.Results: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed RADIAL BASIS Spike Sorter (RBSS) reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity.Conclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

Electroencephalography (EEG) is a major clinical tool to diagnose, monitor and manage neurological disorders which is mostly affected by artifacts. Given the importance and the need for an automated method to remove artifacts, in this paper some intelligent automated methods are proposed which are composed of three main parts as extraction of effective input, filtering and filter optimization. Wavelet transform is utilized to extract the effective input, and the wavelet approximation coefficients are used as an effective input signal. In addition, RADIAL BASIS Function NEURAL Network (RBFNN) has been used for filtering. The appropriate number of RBFs has been selected using extensive simulations, and the optimal value of spread parameter has been achieved by Bees algorithm (BA). Finally, the proposed artifact removal schemes have been evaluated on some real contaminated EEG signals in Mashad Ghaem hospital database. The results show that the proposed artifact removal schemes are able to effectively remove artifacts from EEG signals with little underlying brain signal distortion.

Prediction of peak loads in Iran up to year 2011 is discussed using the RADIAL BASIS Function NETWORKS (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for global grid of Iran. Predictions were done for target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy factors rather than weather conditions. This study focuses on economical data that seem to have influence on long-term electric load demand. The data used are: actual yearly, incremental growth rate from previous year, and blend (actual and incremental growth rate from previous years). As the results, the maximum demands for 2007 through 2011 are predicted and is shown to be elevated from 37138 MW to 45749 MW for Iran Global Grid. The annual average rate of load growth seen per five years until 2011 is about 5.35%.

Until now some methods have been used for stock return forecasting and index return forecasting, and artificial intelligence and NEURAL NETWORKS are one of them. We sought to evaluate the performance of redial BASIS NEURAL NETWORKS to predict the index return. To this purpose, Tehran Stock Exchange index has been used and the performance of RADIAL BASIS function NEURAL network and perceptron NEURAL NETWORKS are compared. Performance testing of NEURAL NETWORKS based on least square error approach in both the in sample forecasting and out sample forecasting. Result of this study showed that for in sample approach RADIAL BASIS function NEURAL network has better performance and for out sample forecasting perceptron. Each one of these methods has strength and weakness of them but in the way we want to use them we can chose each one base on our usage. Base on this research choosing between NNS can be clear for investors and users.

RADIAL BASIS Function NEURAL Network (RBFNN) is a type of artificial NEURAL NETWORKS used for supervised learning. They rely on RADIAL BASIS functions (RBFs), nonlinear mathematical functions employed to approximate complex nonlinear data. Determining the architecture of the network is challenging, impacting the achievement of optimal learning and generalization capacities. This paper presents a multi--objective model for optimizing and training RBFNN architecture. The model aims to fulfill three objectives: the first is the summation of distances between the input vector and the corresponding center for the neurons in the hidden layer. The second objective is the global error of the RBFNN, defined as the discrepancy between the calculated output and the desired output. The third objective is the complexity of the RBFNN, quantified by the number of neurons in the hidden layer. This innovative approach utilizes multiple objective simulated annealing to identify optimal parameters and hyperparameters for NEURAL NETWORKS. The numerical results provide accuracy and reliability of the theoretical results discussed in this paper, as well as advantages of the proposed approach.

Sea level anomaly as a parameter that expresses the difference between the instantaneous water level height and the average amount of water level in a period of time is of great importance in studying the water level situation in different regions. Predicting a time series requires that the series be static and that seasonal trends and changes be removed from the observations to eliminate the dependence of variance and mean on time. For this purpose, the use of various methods to static a time series has been suggested and used. Using the method of decomposition into the intrinsic modes of a signal that underlies the formation of intrinsic mode functions that include parts of the signal with approximately the same frequency,in order to analyze and isolate the trend and seasonal changes of the signal have been considered. Caspian sea as the largest lake in the world or the so-called largest enclosed water area in the world is located in northern Iran. This important water area has become one of the main sources of income for its peripheral countries. It has important oil and gas resources as well as the main source of sturgeon as one of the most expensive food sources in the world. This strategic region is known as a medium for connecting the East and the West of the world. In addition to the economic and commercial dimension, the Caspian Sea is of great importance from the military point of view, as numerous military maneuvers are held every year by the neighboring countries. For the above reasons,awareness of the water level and its changes has become increasingly important, especially over the past few decades, but despite this importance, not many studies have been conducted to study the water level. Therefore, in this research, using satellite altimeter data, the monitoring of water level changes in this area has been done. In this study a coverage of the sea anomaly parameter and its changes from 1993 to the present has been provided. The Caspian Sea water region as one of the two important water sources for Iran, is strategically important. For this purpose, in this study, using the transit data of 92 satellite altimetric missions passing through the Caspian Sea region, the changes in the sea level anomaly in this region since 1993 have been observed. This quantity is then analyzed using the method of analysis of intrinsic modes as an efficient method in separating the frequencies that make up a signal and then, using a NEURAL network, a network of RADIAL base functions has been created in order to predict sea level anomaly. 9 dominant frequencies along with a trend are the result of signal analysis considered in this study. Finally, it leads to the parameters of the mean square error of 0. 029 m and 0. 034 m with a correlation coefficient of 0. 99 and 0. 97, respectively, in the two stages of NEURAL network training and testing.

The Internet of Things (IoT) has become increasingly prevalent, and recent advances in machine learning, particularly in healthcare, have gained significant attention from researchers. One prominent interdisciplinary topic in these fields is human activity recognition (HAR). Despite extensive research, several challenges remain in this area, especially concerning the application of modern machine learning techniques for HAR. This study proposes a novel method for human activity recognition by combining RADIAL BASIS function NEURAL NETWORKS (RBFNN) and support vector machines (SVM). The approach enhances recognition accuracy and algorithm efficiency by extracting relevant features using RBFNN and convolutional NEURAL NETWORKS (CNN). Classification is then performed using SVM. The proposed method was evaluated using the UCI HAR dataset, which includes six distinct human activities. Results demonstrate that the proposed approach achieves an accuracy of 99%, surpassing existing methods.

In this research, computational fluid dynamics method was used to investigate the effect of geometrical parameters of rectangular spiral channels on heat transfer coefficient. Two artificial NEURAL NETWORKS including perceptron (MLP) and RADIAL BASIS function (RBF) models were used to model the heat transfer in helical channels. The model inputs included the Reynolds number and geometric parameters of the channels, and output was the Nusselt number. 135 data were generated by Computational Fluid Dynamics (CFD) simulation and after validation were used for training and evaluation of NEURAL network models. The results of the research showed that the accuracy of MLP was slightly higher than RBF, however, both models were acceptable. Due to the high and acceptable accuracy of these two models, they can be well used in future research and applications. In this research, the main innovation is comparing two different methods for modelling the heat exchanger with a rectangular helical channel. This research shows that the use of perceptron NEURAL network and RADIAL BASIS function can both be effective in improving the performance and efficiency of the heat exchanger. This research can be used as a guide to choose the appropriate method for modeling heat exchangers and help to improve technologies related to this field.

Depth of water at the upstream of pier will increase when a bridge is constructed. The difference between water depth before and after the bridge construction is usually termed as afflux.   The purpose of this paper is to apply and evaluate the abilities of Artificial NEURAL NETWORKS (ANN) to predict this phenomena. Mapping between input data (discharge, normal depth, length to width ratio of bridge pier, contraction ratio and angle of pier axis) and output data (afflux) has been provided by a RADIAL BASIS Function (RBF) ANN based on imperical data produced by experimental measurements. The results show that a RBF artificial NEURAL network including two hidden layers can predict intelligently the afflux and its performance is much better than other conventional approaches.