6,194 research outputs found
Fuzzy neural networks with genetic algorithm-based learning method
This thesis is on the reasoning of artificial neural networks based on granules for both crisp and uncertain data. However, understanding the data in this way is difficult when the data is so complex. Reducing the complexity of the problems that these networks are attempting to learn as well as decreasing the cost of the learning processes are desired for a better prediction. A suitable prediction in artificial neural networks depends on an in-depth understanding of data and fine tracking of relations between data points. Inaccuracies of the prediction are caused by complexity of data set and the complexity is caused by uncertainty and quantity of data. Uncertainties can be represented in granules, and the reasoning based on granules is known as granular computing. This thesis proposed an improvement of granular neural networks to reach an outcome from uncertain and crisp data. Two methods based on genetic algorithms (GAs) are proposed. Firstly, GA-based fuzzy granular neural networks are improved by GA-based fuzzy artificial neural networks. They consist of two parts: granulation using fuzzy c-mean clustering (FCM), and reasoning by GAbased fuzzy artificial neural networks. In order to extract granular rules, a granulation method is proposed. The method has three stages: construction of all possible granular rules, pruning the repetition, and crossing out granular rules. Secondly, the two-phase GA-based fuzzy artificial neural networks are improved by GA-based fuzzy artificial neural networks. They are designed in two phases. In this case, the improvement is based on alpha cuts of fuzzy weight in the network connections. In the first phase, the optimal values of alpha cuts zero and one are obtained to define the place of a fuzzy weight for a network connection. Then, in the second phase, the optimal values of middle alpha cuts are obtained to define the shape of a fuzzy weight. The experiments for the two improved networks are performed in terms of generated error and execution time. The results tested were based on available rule/data sets in University of California Irvine (UCI) machine learning repository. Data sets were used for GA-based fuzzy granular neural networks, and rule sets were used for GA-based fuzzy artificial neural networks. The rule sets used were customer satisfaction, uranium, and the datasets used were wine, iris, servo, concrete compressive strength, and uranium. The results for the two-phase networks revealed the improvements of these methods over the conventional onephase networks. The two-phase GA-based fuzzy artificial neural networks improved 35% and 98% for execution time, and 27% and 26% for the generated error. The results for GA-based granular neural networks were revealed in comparison with GA-based crisp artificial neural networks. The comparison with other related granular computing methods were done using the iris benchmark data set. The results for these networks showed an average performance of 82.1%. The results from the proposed methods were analyzed in terms of statistical measurements for rule strengths and classifier performance using benchmark medical datasets. Therefore, this thesis has shown GA-based fuzzy granular neural networks, and GA-based fuzzy artificial neural networks are capable of reasoning based on granules for both crisp and uncertain data in artificial neural networks
Rule Extraction, Fuzzy ARTMAP, and Medical Databases
This paper shows how knowledge, in the form of fuzzy rules, can be derived from a self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usable set of rules. Simulations on a medical prediction problem, the Pima Indian Diabetes (PID) database, illustrate the method. In the simulations, pruned networks about 1/3 the size of the original actually show improved performance. Quantization yields comprehensible rules with only slight degradation in test set prediction performance.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0083); Institute of Systems Science (National University of Singapore
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
Generation of Explicit Knowledge from Empirical Data through Pruning of Trainable Neural Networks
This paper presents a generalized technology of extraction of explicit
knowledge from data. The main ideas are 1) maximal reduction of network
complexity (not only removal of neurons or synapses, but removal all the
unnecessary elements and signals and reduction of the complexity of elements),
2) using of adjustable and flexible pruning process (the pruning sequence
shouldn't be predetermined - the user should have a possibility to prune
network on his own way in order to achieve a desired network structure for the
purpose of extraction of rules of desired type and form), and 3) extraction of
rules not in predetermined but any desired form. Some considerations and notes
about network architecture and training process and applicability of currently
developed pruning techniques and rule extraction algorithms are discussed. This
technology, being developed by us for more than 10 years, allowed us to create
dozens of knowledge-based expert systems. In this paper we present a
generalized three-step technology of extraction of explicit knowledge from
empirical data.Comment: 9 pages, The talk was given at the IJCNN '99 (Washington DC, July
1999
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability
In this paper a combination of neuro-fuzzy
classifiers for improved classification performance and reliability
is considered. A general fuzzy min-max (GFMM) classifier with
agglomerative learning algorithm is used as a main building
block. An alternative approach to combining individual classifier
decisions involving the combination at the classifier model level is
proposed. The resulting classifier complexity and transparency is
comparable with classifiers generated during a single crossvalidation
procedure while the improved classification
performance and reduced variance is comparable to the ensemble
of classifiers with combined (averaged/voted) decisions. We also
illustrate how combining at the model level can be used for
speeding up the training of GFMM classifiers for large data sets
Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.
In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried out on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed. The rule extraction algorithms, Neurolinear, Neurorule, Trepan and Nefclass, have different characteristics with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree (rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional ifthen rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.Credit; Information systems; International; Systems;
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