Real World Examples of Agent based Decision Support Systems for Deep Learning based on Complex Feed Forward Neural Networks

Abstract

Nature frequently shows us phenomena that in many cases are not fully understood. To research these phenomena we use approaches in computer simulations. This article presents a model based approach for the simulation of human brain functions in order to create recurrent machine learning map fractals that enable the investigation of any problem trained beforehand. On top of a neural network for which each neuron is illustrated with biological capabilities like collection, association, operation, definition and transformation, a thinking model for imagination and reasoning is exemplified in this research. This research illustrates the technical complexity of our dual thinking process in a mathematical and computational way and describes two examples, where an adaptive and self-regulating learning process was applied to real world examples. In conclusion, this research exemplifies how a previously researched conceptual model (SLA process) can be used for making progress to simulate the complex systematics of human thinking processes and gives an overview of the next major steps for making progress on how artificial intelligence can be used to simulate