Intra-Day and Day-Ahead Wind Farm Output Forecasting Using Neural Network Ensembles

Wind energy is an increasingly important component of a utility’s service offerings. Due to the intermittent nature of wind energy, the accuracy of wind farm output forecasts is critical to ensuring optimal integration of wind energy with other sources on a grid. GDF SUEZ has developed an innovative approach to improving the accuracy of wind farm output forecasts which involves developing ensembles of neural networks, each of which is tuned to the characteristics of its target area. An overview of neural network technology and the neural network ensemble modeling process is provided, along with preliminary results based on actual operating data from GDF SUEZ in Lyon France.

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Artificial neural network ensembles for fatigue damage detection in aircraft

Journal of Intelligent Material Systems and Structures ◽

10.1177/1045389x16657428 ◽

2016 ◽

Vol 28 (7) ◽

pp. 851-861 ◽

Cited By ~ 9

Author(s):

Ziemowit Dworakowski ◽

Krzysztof Dragan ◽

Tadeusz Stepinski

Keyword(s):

Neural Network ◽

Neural Networks ◽

Fatigue Cracks ◽

Guided Wave ◽

Multidimensional Data ◽

Neural Network Ensemble ◽

Practical Applications ◽

The Neural Network ◽

Neural Network Ensembles

Neural networks are commonly recognized tools for the classification of multidimensional data obtained in structural health monitoring (SHM) systems. Their configuration for a given scenario is, however, a challenging task, which limits the possibilities of their practical applications. In this article the authors propose using the neural network ensemble approach for the classification of SHM data generated by guided wave sensor networks. The overproduce and choose strategy is used for designing ensembles containing different types and sizes of neural networks. The proposed method allows for a significant increase of the state assessment reliability, which is illustrated by the results obtained from the practical industrial case of a full-scale aircraft test. The method is verified in the process of detecting fatigue cracks propagating in the aircraft load-carrying structure. The long-term experiments are performed in variable environmental conditions with a net of structure-embedded piezoelectric sensors.

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NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026813400063 ◽

2013 ◽

Vol 12 (04) ◽

pp. 1340006 ◽

Cited By ~ 13

Author(s):

YOICHI HAYASHI

Keyword(s):

Neural Network ◽

Neural Networks ◽

High Performance ◽

Real Life ◽

Rule Extraction ◽

Historical Background ◽

Neural Network Ensemble ◽

Extraction Algorithm ◽

Neural Network Ensembles ◽

Network Ensembles

This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situations is obvious. Since the structures of previous neural network ensembles were quite complicated, the research on the efficient rule extraction algorithm from neural network ensembles was few although their learning capability was extremely high. Thus, these issues make rule extraction algorithm for neural network ensemble difficult task. However, there is a practical need for new ideas for neural network ensembles in order to realize the extremely high-performance needs of various rule extraction problems in real life. This paper successively explain nature of artificial neural networks, origin of neural network rule extraction, incorporating fuzziness in neural network rule extraction, theoretical foundation of neural network rule extraction, computational complexity of neural network rule extraction, neuro-fuzzy hybridization, previous rule extraction from neural network ensembles and difficulties of previous neural network ensembles. Next, this paper address three principles of proposed neural network rule extraction: to increase recognition rates, to extract rules from neural network ensembles, and to minimize the use of computing resources. We also propose an ensemble-recursive-rule extraction (E-Re-RX) by two or three standard backpropagation to train multi-layer perceptrons (MLPs), which enabled extremely high recognition accuracy and the extraction of comprehensible rules. Furthermore, this enabled rule extraction that resulted in fewer rules than those in previously proposed methods. This paper summarizes experimental results of rule extraction using E-Re-RX by multiple standard backpropagation MLPs and provides deep discussions. The results make it possible for the output from a neural network ensemble to be in the form of rules, thus open the "black box" of trained neural networks ensembles. Finally, we provide valuable conclusions and as future work, three open questions on the E-Re-RX algorithm.

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RELATIONSHIP BETWEEN DATA SIZE, ACCURACY, DIVERSITY AND CLUSTERS IN NEURAL NETWORK ENSEMBLES

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026813400051 ◽

2013 ◽

Vol 12 (04) ◽

pp. 1340005 ◽

Cited By ~ 2

Author(s):

CHIEN-YUAN CHIU ◽

BRIJESH VERMA

Keyword(s):

Neural Network ◽

Data Sets ◽

Neural Network Ensemble ◽

Cluster Accuracy ◽

Size Accuracy ◽

Benchmark Datasets ◽

Clustering Data ◽

Neural Network Ensembles ◽

Number Of Classes ◽

Network Ensembles

This paper presents an approach for analyzing relationships between data size, cluster, accuracy and diversity in neural network ensembles. The main objective of this research is to find out the influence of data size such as number of patterns, number of inputs and number of classes on various parameters such as clusters, accuracy and diversity of a neural network ensemble. The proposed approach is based on splitting data sets into different groups using the data size, clustering data and conducting training and testing of neural network ensembles. The test data is same for all groups and used to test all trained ensembles. The experiments have been conducted on 15 UCI machine learning benchmark datasets and results are presented in this paper.

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Classifcation of events in information security systems based on neural networks

Open Education ◽

10.21686/1818-4243-2019-1-57-63 ◽

2019 ◽

Vol 23 (1) ◽

pp. 57-63

Author(s):

A. A. Mikryukov ◽

A. V. Babash ◽

V. A. Sizov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Information Security ◽

Integrated Approach ◽

Neural Network Ensemble ◽

Security Systems ◽

Event Classification ◽

Security Event ◽

Neural Network Ensembles ◽

Network Ensembles

Purpose of the research.The aim of the study is to increase the effectiveness of information security and to enhance accuracy and promptness of the classification of security events, security incidents, and threats in information security systems. To respond to this challenge, neural network technologies were suggested as a classification tool for information security systems. These technologies allow accommodating incomplete, inaccurate and unidentified raw data, as well as utilizing previously accumulated information on security issues. To address the problem more effectively, collective methods based on collective neural ensembles aligned with an advanced complex approach were implemented.Materials and methods:When solving complex classification problems, often none of the classification algorithms provides the required accuracy. In such cases, it seems reasonable to build compositions of algorithms, mutually compensating errors of individual algorithms. The study also gives an insight into the application of neural network ensemble to address security issues in the corporate information system and provides a brief review of existing approaches to the construction of neural network ensembles and methods to shape problem solving with neural networks classifiers. An advanced integrated approach is proposed to tackle problems of security event classification based on neural network ensembles (neural network committees). The approach is based on a three-step procedure. The stages of the procedure implementation are described. It is shown that the use of this approach facilitates the efficiency of solving the problem.Results:An advanced integrated approach to addressing security event classification based on neural network ensembles (neural network committees) is proposed. This approach applies adaptive reduction of neural network ensemble (selection of the best classifiers is based on the assessment of the compliance degree of the competence area of the private neural network classifier and convergence of the results of private classifiers), as well as the selection and rationale of the voting method (composition or aggregation of outputs of private classifiers). The results of numerical experiments support the effectiveness of the proposed approach.Conclusion:Collectively used artificial neural networks in the form of neural network ensembles (committees of neural networks) will provide more accurate and reliable results of security event classification in the corporate information network. Moreover, an advanced integrated approach to the construction of a neural network ensemble is proposed to facilitate effectiveness of the classification process. The approach is based on the application of the adaptive reduction procedure for the results of private classifiers and the procedure for selecting the method of aggregation of the results of private classifiers. These outcomes will enable advancement of the system control over information security incidents. Finally, the paper defines tendencies and directions of the development of collective solution methods applying neural network ensembles (committees of neural networks).

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Pareto Ensembles for Evolutionary Synthesis of Neurocontrollers in a 2D Maze-Based Video Game

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.3173 ◽

2013 ◽

Vol 284-287 ◽

pp. 3173-3177

Author(s):

Tse Guan Tan ◽

Jason Teo ◽

Kim On Chin ◽

Patricia Anthony

Keyword(s):

Neural Network ◽

Evolutionary Algorithm ◽

Video Game ◽

Neural Network Ensemble ◽

Multi Objective ◽

Neural Ensemble ◽

Conflicting Objectives ◽

Baseline System ◽

Neural Network Ensembles ◽

Network Ensembles

In this paper, we present a study of evolving artificial neural network controllers for autonomously playing maze-based video game. A system using multi-objective evolutionary algorithm is developed, which is called as Pareto Archived Evolution Strategy Neural Network (PAESNet), with the attempt to find a set of Pareto optimal solutions by simultaneously optimizing two conflicting objectives. The experiments are designed to address two research aims investigating: (1) evolving weights (including biases) of the connections between the neurons and structure of the network through multi-objective evolutionary algorithm in order to reduce its runtime operation and complexity, (2) improving the generalization ability of the networks by using neural network ensemble model. A comparative analysis between the single network model as the baseline system and the model built based on the neural ensemble are presented. The evidence from this study suggests that Pareto multi-objective paradigm and neural network ensembles can be effective for creating and controlling the behaviors of video game characters.

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