An analysis of the neural representation of birdsong memory.

Neuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...

Birdsong Memory: A Neural Dissociation between Song Recognition and Production

Songbirds learn their song from an adult conspecific tutor when they are young, much like the acquisition of speech in human infants. When an adult zebra finch is re-exposed to its tutor's song, there is increased neuronal activation in the caudomedial nidopallium (NCM), the songbird equivalent of the auditory association cortex. This neuronal activation is related to the fidelity of song imita...

A Novel Fuzzy and Artificial Neural Network Representation of Overcurrent Relay Characteristics

Accurate models of Overcurrent (OC) with inverse time relay characteristics play an important role for coordination of power system protection schemes. This paper proposes a new method for modeling OC relays curves. The model is based on fuzzy logic and artificial neural networks. The feed forward multilayer perceptron neural network is used to calculate operating times of OC relays for various...

Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network

Named Entity Recognition in Persian Text using Deep Learning

Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...

A METAHEURISTIC-BASED ARTIFICIAL NEURAL NETWORK FOR PLASTIC LIMIT ANALYSIS OF FRAMES

Despite the advantages of the plastic limit analysis of structures, this robust method suffers from some drawbacks such as intense computational cost. Through two recent decades, metaheuristic algorithms have improved the performance of plastic limit analysis, especially in structural problems. Additionally, graph theoretical algorithms have decreased the computational time of the process impre...