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Dettagli della rivista
Formato
Rivista
eISSN
2449-6499
Pubblicato per la prima volta
30 Dec 2014
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

Volume 8 (2018): Edizione 2 (April 2018)

Dettagli della rivista
Formato
Rivista
eISSN
2449-6499
Pubblicato per la prima volta
30 Dec 2014
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

5 Articoli
Accesso libero

Simulation and Experimental Evaluation of the EKF Simultaneous Localization and Mapping Algorithm on the Wifibot Mobile Robot

Pubblicato online: 01 Nov 2017
Pagine: 91 - 101

Astratto

Abstract

In recent years, autonomous navigation for mobile robots has been considered a highly active research field. Within this context, we are interested to apply the Simultaneous Localization And Mapping (SLAM) approach for a wheeled mobile robot. The Extended Kalman Filter has been chosen to perform the SLAM algorithm. In this work, we explicit all steps of the approach. Performances of the developed algorithm have been assessed through simulation in the case of a small scale map. Then, we present several experiments on a real robot that are proceeded in order to exploit a programmed SLAM unit and to generate the navigation map. Based on experimental results, simulation of the SLAM method in the case of a large scale map is then realized. Obtained results are exploited in order to evaluate and compare the algorithm’s consistency and robustness for both cases.

Parole chiave

  • mobile robot
  • localisation
  • EKF
  • SLAM
  • consistency
Accesso libero

Particle Swarm Optimization for Solving a Class of Type-1 and Type-2 Fuzzy Nonlinear Equations

Pubblicato online: 01 Nov 2017
Pagine: 103 - 110

Astratto

Abstract

This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case.

Parole chiave

  • type1 and type2 fuzzy sets
  • polynomial and exponential equations
  • particle swarm optimization
Accesso libero

Texture and Gene Expression Analysis of the MRI Brain in Detection of Alzheimer’s Disease

Pubblicato online: 01 Nov 2017
Pagine: 111 - 120

Astratto

Abstract

Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.

Parole chiave

  • Alzheimer’s Disease
  • MRI
  • Feature Extraction
  • Bi-Clustering
  • Local Binary Pattern (LBP)
Accesso libero

An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

Pubblicato online: 01 Nov 2017
Pagine: 121 - 132

Astratto

Abstract

Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.

Parole chiave

  • High order artificial neural networks
  • pi-sigma neural network
  • forecasting
  • recurrent neural network
  • Particle Swarm Optimization
Accesso libero

Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks

Pubblicato online: 01 Nov 2017
Pagine: 133 - 154

Astratto

Abstract

Conceptual or explanatory models are a key element in the process of complex system modelling. They not only provide an intuitive way for modellers to comprehend and scope the complex phenomena under investigation through an abstract representation but also pave the way for the later development of detailed and higher-resolution simulation models. An evolutionary echo state network-based method for supporting the development of such models, which can help to expedite the generation of alternative models for explaining the underlying phenomena and potentially reduce the manual effort required, is proposed. It relies on a customised echo state neural network for learning sparse conceptual model representations from the observed data. In this paper, three evolutionary algorithms, a genetic algorithm, differential evolution and particle swarm optimisation are applied to optimize the network design in order to improve model learning. The proposed methodology is tested on four examples of problems that represent complex system models in the economic, ecological and physical domains. The empirical analysis shows that the proposed technique can learn models which are both sparse and effective for generating the output that matches the observed behaviour.

Parole chiave

  • Complex systems modelling
  • Conceptual models
  • Causal loop diagrams
  • Computational intelligence
  • Echo state networks
  • Evolutionary algorithms
5 Articoli
Accesso libero

Simulation and Experimental Evaluation of the EKF Simultaneous Localization and Mapping Algorithm on the Wifibot Mobile Robot

Pubblicato online: 01 Nov 2017
Pagine: 91 - 101

Astratto

Abstract

In recent years, autonomous navigation for mobile robots has been considered a highly active research field. Within this context, we are interested to apply the Simultaneous Localization And Mapping (SLAM) approach for a wheeled mobile robot. The Extended Kalman Filter has been chosen to perform the SLAM algorithm. In this work, we explicit all steps of the approach. Performances of the developed algorithm have been assessed through simulation in the case of a small scale map. Then, we present several experiments on a real robot that are proceeded in order to exploit a programmed SLAM unit and to generate the navigation map. Based on experimental results, simulation of the SLAM method in the case of a large scale map is then realized. Obtained results are exploited in order to evaluate and compare the algorithm’s consistency and robustness for both cases.

Parole chiave

  • mobile robot
  • localisation
  • EKF
  • SLAM
  • consistency
Accesso libero

Particle Swarm Optimization for Solving a Class of Type-1 and Type-2 Fuzzy Nonlinear Equations

Pubblicato online: 01 Nov 2017
Pagine: 103 - 110

Astratto

Abstract

This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case.

Parole chiave

  • type1 and type2 fuzzy sets
  • polynomial and exponential equations
  • particle swarm optimization
Accesso libero

Texture and Gene Expression Analysis of the MRI Brain in Detection of Alzheimer’s Disease

Pubblicato online: 01 Nov 2017
Pagine: 111 - 120

Astratto

Abstract

Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.

Parole chiave

  • Alzheimer’s Disease
  • MRI
  • Feature Extraction
  • Bi-Clustering
  • Local Binary Pattern (LBP)
Accesso libero

An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

Pubblicato online: 01 Nov 2017
Pagine: 121 - 132

Astratto

Abstract

Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.

Parole chiave

  • High order artificial neural networks
  • pi-sigma neural network
  • forecasting
  • recurrent neural network
  • Particle Swarm Optimization
Accesso libero

Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks

Pubblicato online: 01 Nov 2017
Pagine: 133 - 154

Astratto

Abstract

Conceptual or explanatory models are a key element in the process of complex system modelling. They not only provide an intuitive way for modellers to comprehend and scope the complex phenomena under investigation through an abstract representation but also pave the way for the later development of detailed and higher-resolution simulation models. An evolutionary echo state network-based method for supporting the development of such models, which can help to expedite the generation of alternative models for explaining the underlying phenomena and potentially reduce the manual effort required, is proposed. It relies on a customised echo state neural network for learning sparse conceptual model representations from the observed data. In this paper, three evolutionary algorithms, a genetic algorithm, differential evolution and particle swarm optimisation are applied to optimize the network design in order to improve model learning. The proposed methodology is tested on four examples of problems that represent complex system models in the economic, ecological and physical domains. The empirical analysis shows that the proposed technique can learn models which are both sparse and effective for generating the output that matches the observed behaviour.

Parole chiave

  • Complex systems modelling
  • Conceptual models
  • Causal loop diagrams
  • Computational intelligence
  • Echo state networks
  • Evolutionary algorithms

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