39,865 research outputs found
A computational intelligence approach to modelling interstate conflict : Forecasting and causal interpretations
The quantitative study of conflict management is concerned with finding models
which are accurate and also capable of providing a causal interpretation of results.
This dissertation applies computational intelligence methods to study interstate disputes.
Both multilayer perceptron neural networks and Takagi-Sugeno neuro-fuzzy
models are used to model interstate interactions. The multilayer perceptron neural
network is trained in the Bayesian framework, using the Hybrid Monte Carlo method
to sample from the posterior probabilities. It is found that the network is able to
forecast conflict with an accuracy of 77.3%. A hybrid machine learning method using
the neural network and the genetic algorithm is then presented as a method of
suggesting how conflict can be brought under control. The automatic relevance determination
approach and the sensitivity analysis are used as methods of extracting
causal information from the neural network. The Takagi-Sugeno neuro-fuzzy model
is optimised, using the Gustafson-Kessel clustering algorithm to partion the input
space. It is found that the neuro-fuzzy model predicts conflict with an accuracy of
80.1%. The neuro-fuzzy model is also incorporated into the hybrid machine learning
method to suggest how the identified conflict cases can be avoided. The casual
interpretation is then formulated by a linguistic approximation of the fuzzy rules
extracted from the neuro-fuzzy model. The major finding in this work is that the
interpretations drawn from both the neural network and the neuro-fuzzy model are
consistent
Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network.
Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Prediction in Photovoltaic Power by Neural Networks
The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches
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