precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

abstract to get ride of fragile and unsustainable single product export, a comprehensive knowledge of export potential and comparative advantage is required. agricultural products can be considered as a suitable target for this purpose. for more efficient planning for agricultural products export, proper forecasting is necessary. to achieve this goal, two methods were used and compared. first, ...

Neural systems process information in a dynamical regime between silence and chaotic dynamics. This has lead to the criticality hypothesis which suggests that neural reach such state by self-organizing towards critical point of phase transition. Here, we study minimal network model exhibits self-organized presence stochastic noise using rewiring rule only utilizes local information. For evoluti...

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm ...

today, stock investment has become an important mean of national finance. apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. in the past, long-term trading processes and many technical analysis methods for stock market were put forward. however, stock market is a nonlinear sys...

Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by from training examples of noisy-clean pairs. It has become the go-to methodology for tackling and outperformed traditional non-local class methods. However, top-performing are generally composed many layers hundreds neurons, with trainable parameters in excess several million. We claim tha...

Long-term prediction of precipitation in planning and managing water resources, especially in arid and semi-arid countries such as Iran, has a great importance. In this paper, a method for predicting long-term precipitation using weather signals and artificial neural networks is presented. For this purpose, climatic data (large-scale signals) and meteorological data (local precipitation and tem...