Content: A neural network is a set of algorithms that are modeled loosely after the human brain in recognizing patterns. They interpret sensory data through a kind of machine that perceive, marks or clusters the original input. The patterns they recognize are digital and are included in vectors, which contained all real-world data such as images, sounds, text or time series.
The neural network is a computational model based on the structure and function of the biological neural network. The information flowing through the network affects the structure of the neural network because neural network changes or learns, in a sense, based on input and output. Although the neural network is very complex, such as the weight change of each new data in the time range, an experimental model of the high-level architecture of the neural processor is proposed. The neural processor performs all the functions of a common neural network, such as adaptive learning, self-organization, real-time operation, and fault tolerance.
Indian system engineer Manu Mitra studied the neural processor problem in artificial intelligence advancement, which was published in the journal - Journal of Autonomous Intelligence. This paper discusses the analysis of neural processing and introduces it through experiments, including graphical representations of data analysis.