Deep Evolving Stacking Convex Cascade Neo-Fuzzy Network and its Rapid Learning

A deep evolving stacking convex neo-fuzzy network is proposed. It is a feedforward cascade hybrid system, the layers-stacks of which are formed by generalized neo-fuzzy neurons that implement Wang-Mendel fuzzy reasoning. The optimal in the sense of speed algorithms are proposed for its learning. Due to independent layer adjustment, parallelization of calculations in non-linear synapses and optimization of learning processes, the proposed network has high speed that allows to process information in online mode.

• Publication year

  2018

• Venue

  Conference on Computer Science and Information Systems

• Publication date

  2018-09-26

• Fields of study

  Computer Science

• Identifiers
  DOI 10.15439/2018F200
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Deep learning for neural networks

  2018cited by this paper
• Adaptive multivariate hybrid neuro-fuzzy system and its on-board fast learning

  2017cited by this paper
• Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks

  2017cited by this paper
• Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks

  2016cited by this paper
• Deep evolving GMDH-SVM-neural network and its learning for Data Mining tasks

  2016cited by this paper
• Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks

  2016cited by this paper
• Hybrid Generalized Additive Wavelet-Neuro-Fuzzy-System and Its Adaptive Learning

  2016cited by this paper
• Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms

  2015cited by this paper
• Deep Learning

  2015cited by this paper
• Hybrid neuro-neo-fuzzy system and its adaptive learning algorithm

  2015cited by this paper
• Deep learning in neural networks: An overview

  2014cited by this paper
• A hybrid cascade neural network with an optimized pool in each cascade

  2014cited by this paper
• I and J

  2012cited by this paper
• Deep Convex Net: A Scalable Architecture for Speech Pattern Classification

  2011cited by this paper
• Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams

  2010cited by this paper
• Influence of cultivation temperature on the ligninolytic activity of selected fungal strains

  2006cited by this paper
• Data Streams: Models and Algorithms (Advances in Database Systems)

  2006cited by this paper
• A new learning algorithm for a forecasting neuro-fuzzy network

  2003cited by this paper
• Evolving Connectionist Systems

  2003cited by this paper
• An Adaptive Learning Algorithm for a Neuro-fuzzy Network

  2001cited by this paper
• I and i

  2001cited by this paper
• Analog Implementation of Neo-Fuzzy Neuron and Its On-board Learning

  1999influential reference
• Stacked generalization

  1992cited by this paper
• The Cascade-Correlation Learning Architecture

  1989cited by this paper
• Generalized Additive Models

  1986cited by this paper

• Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks

  2021cites this paper
• Matrix Neo-Fuzzy-System and its Online Learning in Image Recognition Task

  2021cites this paper