ISSN: 0893-6080

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Neural Networks Q1 Unclaimed

Elsevier Ltd. United Kingdom

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Neural Networks is a journal indexed in SJR in Cognitive Neuroscience and Artificial Intelligence with an H index of 148. It has an SJR impact factor of 1,396 and it has a best quartile of Q1. It is published in English. It has an SJR impact factor of 1,396.

Neural Networks focuses its scope in these topics and keywords: neural, learning, network, networks, ganglia, functionsthe, implementation, kernelsa, mapcostefficient, largescale, ...

Type: Journal

Type of Copyright:

Languages: English

Open Access Policy:

Type of publications:

Publication frecuency: -

Categories: Artificial Intelligence (Q1) Cognitive Neuroscience (Q1)

Price

- €

Inmediate OA

NPD

Embargoed OA

- €

Non OA

Metrics

1,396

SJR Impact factor

148

H Index

373

Total Docs (Last Year)

580

Total Docs (3 years)

19774

Total Refs

6106

Total Cites (3 years)

575

Citable Docs (3 years)

11,02

Cites/Doc (2 years)

53,01

Ref/Doc

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Aims and Scope

Best articles by citations

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Rule-extraction by backpropagation of polyhedra

A real-time, unsupervised neural network for the low-level control of a mobile robot in a nonstationary environment

Conformal self-organization for continuity on a feature map

A learning algorithm for applying synthesized stable dynamics to system identification

How good are support vector machines?

Global bifurcation structure of chaotic neural networks and its application to traveling salesman problems

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A learning algorithm for oscillatory cellular neural networks

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Self-organization using Potts models

A remark on 'On stability of nonlinear continuous-time neural networks with delays'

A neural network for 500 word vocabulary word spotting using non-uniform units

Network constraints and multi-objective optimization for one-class classification

A fast neural-network algorithm for VLSI cell placement

An efficient learning algorithm for improving generalization performance of radial basis function neural networks

Density Estimation Through Convex Combinations of Densities: Approximation and Estimation Bounds

Learning a class of large finite state machines with a recurrent neural network

An Efficient Mapping of Fuzzy ART onto a Neural Architecture