1,368 research outputs found
Extracting Boolean rules from CA patterns
A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic nois
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
Learning to Act through Evolution of Neural Diversity in Random Neural Networks
Biological nervous systems consist of networks of diverse, sophisticated
information processors in the form of neurons of different classes. In most
artificial neural networks (ANNs), neural computation is abstracted to an
activation function that is usually shared between all neurons within a layer
or even the whole network; training of ANNs focuses on synaptic optimization.
In this paper, we propose the optimization of neuro-centric parameters to
attain a set of diverse neurons that can perform complex computations.
Demonstrating the promise of the approach, we show that evolving neural
parameters alone allows agents to solve various reinforcement learning tasks
without optimizing any synaptic weights. While not aiming to be an accurate
biological model, parameterizing neurons to a larger degree than the current
common practice, allows us to ask questions about the computational abilities
afforded by neural diversity in random neural networks. The presented results
open up interesting future research directions, such as combining evolved
neural diversity with activity-dependent plasticity.Comment: Linebreaks in abstract fixe
Evolutionary Construction of Convolutional Neural Networks
Neuro-Evolution is a field of study that has recently gained significantly
increased traction in the deep learning community. It combines deep neural
networks and evolutionary algorithms to improve and/or automate the
construction of neural networks. Recent Neuro-Evolution approaches have shown
promising results, rivaling hand-crafted neural networks in terms of accuracy.
A two-step approach is introduced where a convolutional autoencoder is created
that efficiently compresses the input data in the first step, and a
convolutional neural network is created to classify the compressed data in the
second step. The creation of networks in both steps is guided by by an
evolutionary process, where new networks are constantly being generated by
mutating members of a collection of existing networks. Additionally, a method
is introduced that considers the trade-off between compression and information
loss of different convolutional autoencoders. This is used to select the
optimal convolutional autoencoder from among those evolved to compress the data
for the second step. The complete framework is implemented, tested on the
popular CIFAR-10 data set, and the results are discussed. Finally, a number of
possible directions for future work with this particular framework in mind are
considered, including opportunities to improve its efficiency and its
application in particular areas
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey
Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally. (C) 2019 Published by Elsevier Inc.Igor Škrjanc, Jose Antonio Iglesias and Araceli Sanchis would like to thank to the Chair of Excellence of Universidad Carlos III de Madrid, and the Bank of Santander Program for their support. Igor Škrjanc is grateful to Slovenian Research Agency with the research program P2-0219, Modeling, simulation and control. Daniel Leite acknowledges the Minas Gerais Foundation for Research and Development (FAPEMIG), process APQ-03384-18. Igor Škrjanc and Edwin Lughofer acknowledges the support by the ”LCM — K2 Center for Symbiotic Mechatronics” within the framework of the Austrian COMET-K2 program. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grant
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