This paper makes an exhaustive survey of various applications of Quantum inspired computational intelligence (QCI) techniques proposed till date Definition, categorization and motivation for QCI techniques are stated clearly Major Drawbacks and challenges are discussed The significance of this work is that it presents an overview on applications of QCI in solving various problems in engineering, which will be very much useful for researchers on Quantum computing in exploring this upcoming and young discipline

Applications of quantum inspired computational intelligence: a survey

Feature extraction techniques for input representation to diagnose infant diseases have received significant attention recently Mel Frequency Cepstral Coefficients (MFCC) is one of the most popular feature extraction techniques due to its representation method being very similar to the human auditory system The MFCC method for feature extraction depends on several important parameter settings, namely the number of filter banks, and the number of coefficients used in the final representation These settings affects the way the features are represented, and in turn, affects the performance of the classifier for diagnosis of the disease In this paper, the Particle Swarm Optimization (PSO) algorithm was used to optimise the parameters of the MFCC feature extraction method for classifying infants with asphyxia The extracted MFCC features were then used to train several MLP classifiers over different initialization values The accuracy of these classifiers was then used to guide the PSO optimization Our results show that the optimization of MFCC computation using PSO yielded 939% accuracy, an improvement of 145% over typical MFCC parameter settings using the same classifier

Particle Swarm Optimisation of Mel-frequency Cepstral Coefficients computation for the classification of asphyxiated infant cry

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - DEPSO is introduced. DEPSO is a standard particle swarm optimization (PSO) algorithm with the addition of a differential evolution step to aid in swarm convergence. The results from DEPSO are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but DEPSO provides better learning capabilities for the functions generated by MLPs and SRNs as compared to BP and PSO. These three algorithms are also trained on several benchmark functions to confirm results.

Learning nonlinear functions with MLPs and SRNs

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) and two benchmark functions are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - PSO-QI is introduced. PSO-QI is a standard particle swarm optimization (PSO) algorithm with the addition of a quantum step utilizing the probability density property of a quantum particle. The results from PSO-QI are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but PSO-QI provides learning capabilities of these functions by MLPs and SRNs compared to BP and PSO.

/pdf/learning-functions-generated-by-randomly-initialized-mlps-5fif3o6pli.pdf

Learning functions generated by randomly initialized MLPs and SRNs

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Learning nonlinear functions with MLPs and SRNs

Learning functions generated by randomly initialized MLPs and SRNs