20,584 research outputs found
Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms
Evolutionary artificial neural networks (EANNs) refer to a special class of
artificial neural networks (ANNs) in which evolution is another fundamental
form of adaptation in addition to learning. Evolutionary algorithms are used to
adapt the connection weights, network architecture and learning algorithms
according to the problem environment. Even though evolutionary algorithms are
well known as efficient global search algorithms, very often they miss the best
local solutions in the complex solution space. In this paper, we propose a
hybrid meta-heuristic learning approach combining evolutionary learning and
local search methods (using 1st and 2nd order error information) to improve the
learning and faster convergence obtained using a direct evolutionary approach.
The proposed technique is tested on three different chaotic time series and the
test results are compared with some popular neuro-fuzzy systems and a recently
developed cutting angle method of global optimization. Empirical results reveal
that the proposed technique is efficient in spite of the computational
complexity
Model fusion using fuzzy aggregation: Special applications to metal properties
To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments
Comparative performance of some popular ANN algorithms on benchmark and function approximation problems
We report an inter-comparison of some popular algorithms within the
artificial neural network domain (viz., Local search algorithms, global search
algorithms, higher order algorithms and the hybrid algorithms) by applying them
to the standard benchmarking problems like the IRIS data, XOR/N-Bit parity and
Two Spiral. Apart from giving a brief description of these algorithms, the
results obtained for the above benchmark problems are presented in the paper.
The results suggest that while Levenberg-Marquardt algorithm yields the lowest
RMS error for the N-bit Parity and the Two Spiral problems, Higher Order
Neurons algorithm gives the best results for the IRIS data problem. The best
results for the XOR problem are obtained with the Neuro Fuzzy algorithm. The
above algorithms were also applied for solving several regression problems such
as cos(x) and a few special functions like the Gamma function, the
complimentary Error function and the upper tail cumulative
-distribution function. The results of these regression problems
indicate that, among all the ANN algorithms used in the present study,
Levenberg-Marquardt algorithm yields the best results. Keeping in view the
highly non-linear behaviour and the wide dynamic range of these functions, it
is suggested that these functions can be also considered as standard benchmark
problems for function approximation using artificial neural networks.Comment: 18 pages 5 figures. Accepted in Pramana- Journal of Physic
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
Hybrid LQG-Neural Controller for Inverted Pendulum System
The paper presents a hybrid system controller, incorporating a neural and an
LQG controller. The neural controller has been optimized by genetic algorithms
directly on the inverted pendulum system. The failure free optimization process
stipulated a relatively small region of the asymptotic stability of the neural
controller, which is concentrated around the regulation point. The presented
hybrid controller combines benefits of a genetically optimized neural
controller and an LQG controller in a single system controller. High quality of
the regulation process is achieved through utilization of the neural
controller, while stability of the system during transient processes and a wide
range of operation are assured through application of the LQG controller. The
hybrid controller has been validated by applying it to a simulation model of an
inherently unstable system of inverted pendulum
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