scholarly journals Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method

2010 ◽  
Vol 2010 ◽  
pp. 1-23 ◽  
Author(s):  
Laercio B. Gonçalves ◽  
Fabiana R. Leta
Keyword(s):  
Image Classification ◽  
Texture Classification ◽  
Space Partitioning ◽  
Neuro Fuzzy ◽  
Binary Space Partitioning ◽  
Decision Structure ◽  
Rgb Images ◽  
Texture Image Classification ◽  
Rock Texture

We used a Hierarchical Neuro-Fuzzy Class Method based on binary space partitioning (NFHB-Class Method) for macroscopic rock texture classification. The relevance of this study is in helping Geologists in the diagnosis and planning of oil reservoir exploration. The proposed method is capable of generating its own decision structure, with automatic extraction of fuzzy rules. These rules are linguistically interpretable, thus explaining the obtained data structure. The presented image classification for macroscopic rocks is based on texture descriptors, such as spatial variation coefficient, Hurst coefficient, entropy, and cooccurrence matrix. Four rock classes have been evaluated by the NFHB-Class Method: gneiss (two subclasses), basalt (four subclasses), diabase (five subclasses), and rhyolite (five subclasses). These four rock classes are of great interest in the evaluation of oil boreholes, which is considered a complex task by geologists. We present a computer method to solve this problem. In order to evaluate system performance, we used 50 RGB images for each rock classes and subclasses, thus producing a total of 800 images. For all rock classes, the NFHB-Class Method achieved a percentage of correct hits over 73%. The proposed method converged for all tests presented in the case study.

Hierarchical Neuro-Fuzzy Systems Part II

2011 ◽  
pp. 817-824
Author(s):  
Marley Vellasco ◽  
Marco Pacheco ◽  
Karla Figueiredo ◽  
Flavio Souza
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Learning Process ◽  
Fuzzy Systems ◽  
Space Partitioning ◽  
Learning Methods ◽  
New Class ◽  
Neuro Fuzzy ◽  
Binary Space Partitioning ◽  
Priori Information

This paper describes a new class of neuro-fuzzy models, called Reinforcement Learning Hierarchical Neuro- Fuzzy Systems (RL-HNF). These models employ the BSP (Binary Space Partitioning) and Politree partitioning of the input space [Chrysanthou,1992] and have been developed in order to bypass traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure and rules (ANFIS [Jang,1997], NEFCLASS [Kruse,1995] and FSOM [Vuorimaa,1994]). These new models, named Reinforcement Learning Hierarchical Neuro-Fuzzy BSP (RL-HNFB) and Reinforcement Learning Hierarchical Neuro-Fuzzy Politree (RL-HNFP), descend from the original HNFB that uses Binary Space Partitioning (see Hierarchical Neuro-Fuzzy Systems Part I). By using hierarchical partitioning, together with the Reinforcement Learning (RL) methodology, a new class of Neuro-Fuzzy Systems (SNF) was obtained, which executes, in addition to automatically learning its structure, the autonomous learning of the actions to be taken by an agent, dismissing a priori information (number of rules, fuzzy rules and sets) relative to the learning process. These characteristics represent an important differential when compared with existing intelligent agents learning systems, because in applications involving continuous environments and/or environments considered to be highly dimensional, the use of traditional Reinforcement Learning methods based on lookup tables (a table that stores value functions for a small or discrete state space) is no longer possible, since the state space becomes too large. This second part of hierarchical neuro-fuzzy systems focus on the use of reinforcement learning process. The first part presented HNFB models based on supervised learning methods. The RL-HNFB and RL-HNFP models were evaluated in a benchmark control application and a simulated Khepera robot environment with multiple obstacles.

scholarly journals Modelo Neuro-Fuzzy Hierárquico Politree com aprendizado por reforço para agentes inteligentes

2007 ◽  
Vol 18 (2) ◽  
pp. 234-250 ◽  
Author(s):  
Karla Figueiredo ◽  
Marley Vellasco ◽  
Marco Pacheco ◽  
Flávio Souza
Keyword(s):  
Reinforcement Learning ◽  
Space Partitioning ◽  
Neuro Fuzzy ◽  
Binary Space Partitioning

Este trabalho apresenta um novo modelo híbrido neuro-fuzzy para aprendizado automático de ações efetuadas por agentes. O objetivo do modelo é dotar um agente de inteligência, tornando-o capaz de, através da interação com o seu ambiente, adquirir e armazenar o conhecimento e raciocinar (inferir uma ação). Este novo modelo, denominado Reinforcement Learning Neuro-Fuzzy Hierárquico Politree (RL-NFHP), descende dos modelos neuro-fuzzy hierárquicos NFHB, os quais utilizam aprendizado supervisionado e particionamento BSP (Binary Space Partitioning) do espaço de entrada. Com o uso desse método hierárquico de particionamento, associado ao Reinforcement Learning, obteve-se uma nova classe de Sistemas Neuro-Fuzzy (SNF) que executam, além do aprendizado da estrutura, o aprendizado autônomo das ações a serem tomadas por um agente. Essas características representam um importante diferencial em relação aos sistemas de aprendizado de agentes inteligentes existentes. O modelo RL-NFHP foi testado em diferentes problemas benchmark e em uma aplicação de robótica (robô Khepera). Os resultados obtidos mostram o potencial do modelo proposto, que dispensa informações preliminares como número e formato das regras, e número de partições que o espaço de entrada deve possuir.

scholarly journals Improved texture image classification through the use of a corrosion-inspired cellular automaton

2015 ◽  
Vol 149 ◽  
pp. 1560-1572 ◽  
Author(s):  
Núbia Rosa da Silva ◽  
Pieter Van der Weeën ◽  
Bernard De Baets ◽  
Odemir Martinez Bruno
Keyword(s):  
Cellular Automaton ◽  
Image Classification ◽  
Texture Image ◽  
Texture Image Classification

Efficient learning with augmented spikes: A case study with image classification

2021 ◽  
Author(s):  
Shiming Song ◽  
Chenxiang Ma ◽  
Wei Sun ◽  
Junhai Xu ◽  
Jianwu Dang ◽  
...  
Keyword(s):  
Image Classification ◽  
Efficient Learning

scholarly journals Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

2017 ◽  
Vol 2017 ◽  
pp. 1-22
Author(s):  
Danping Wang ◽  
Kunyuan Hu ◽  
Lianbo Ma ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Tests ◽  
Particle Swarm ◽  
Local Optimum ◽  
Space Partitioning ◽  
Swarm Optimization ◽  
Real World Problem ◽  
Binary Space Partitioning ◽  
Solution Accuracy ◽  
Search History

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.

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