The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this pa...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various datasets by California Department Transportation, and employs variety standard algorithms (LASSO regression, shallow deep neural networks, stacked autoencoders, convolutional, recurrent networks) comparison purpose...

This paper presents to model the Hebb learning rule and proposes a neuron learning machine (NLM). Hebb learning rule describes the plasticity of the connection between presynaptic and postsynaptic neurons and it is unsupervised itself. It formulates the updating gradient of the connecting weight in artificial neural networks. In this paper, we construct an objective function via modeling the He...

In Machine learning and artificial intelligence have seemingly never been as typical and relevant to real-time applications as they are in these days autonomous, big data era. The fortune of machine learning and artificial intelligence depends on the coexistence of three important conditions: powerful computing environments, rich and/or large data, and efficient learning techniques (algorithms)...