International Journal of Computational Intelligence and Applications

The application of neural networks (NNs) to problems of prediction has become increasingly popular. This paper presents a modified hybrid adaptive neural network with revised adaptive smoothing errors, based on a genetic algorithm, and using modified adaptive relaxation to build a learning system for complex problem solving in yield prediction. This system predicts weekly yield values of a tomato crop using environmental variables measured inside the greenhouse as inputs. The proposed learning system is an intelligent computing technique and the numerical values of the neural network connection weights are modified through a training algorithm, using a modified optimization approach. The paper further presents an analysis of the convergence rate of the error in a neural network. The method is evaluated using datasets from a tomato producer, so as to test the predictive ability of the method and compare it with standard models. The results show a comparatively good level of accuracy.

Reliability data is essential for a nuclear power plant probabilistic safety assessment by fault tree analysis to assess the performance of the safety-related systems. The limitation of conventional reliability data arises from insufficient historical data for probabilistic calculation. This study describes a new approach to calculate nuclear event reliability data by utilizing the concept of failure possibilities, which are expressed in qualitative natural languages, mathematically represented by membership functions of fuzzy numbers, and subjectively justified by a group of experts based on their working experience and expertise. We also propose an area defuzzification technique to convert the membership function into nuclear event reliability data. The actual event reliability data, which are collected from the operational experiences of the reactor protection system in Babcock & Wilcox pressurized water reactor between 1984 and 1998, are then compared with the reliability data calculated from the new approach. The results show that fuzzy failure rates can be used as alternatives for probabilistic failure rates when nuclear event historical data are insufficient or unavailable for probabilistic calculation. This study also confirms that our proposed area defuzzification technique is a suitable technique to defuzzify failure possibilities into nuclear event reliability data.

Multiple sequence alignment (MSA) is one of the basic and important problems in molecular biology. MSA can be used for different purposes including finding the conserved motifs and structurally important regions in protein sequences and determine evolutionary distance between sequences. Aligning several sequences cannot be done in polynomial time and therefore heuristic methods such as genetic algorithms can be used to find approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. Most of these algorithms use very complicated, problem specific and time consuming mutation operators. In this paper, we propose a new algorithm that uses a new way of population initialization and simple mutation and recombination operators. The strength of the proposed GA is using simple mutation operators and also a special recombination operator that does not have problems of similar recombination operators in other GAs. The experimental results show that the proposed algorithm is capable of finding good MSAs in contrast to existing methods, while it uses simple operators with low computational complexity.

A scalable modular neural network array architecture has been proposed for real time damage detection in plate like structures for structural health monitoring applications. Damages in a plate like structure are simulated using finite element method of numeric system simulation. Various damage states are numerically simulated by varying Young's modulus of the material at various locations of the structure. Transient vibratory loads are applied at one end of the beam and picked at the other end by means of point sensors. The vibration signals thus obtained are then filtered and subjected to wavelet transform (WT) based multi resolution analysis (MRA) to extract features and identify them. The redundant features are removed and only the principal features are retained using principal component analysis (PCA). A large database of principal features (the feature base) corresponding to different damage scenarios is created. This feature base is used to train individual multi layer perceptron (MLP) networks to identify different parameters of the damage such as location and extent (Young's modulus). Individually trained MLP units are then organized and connected in parallel so that different damage parameters can be identified almost simultaneously, on being fed with new signal feature vectors. For a given case, damage classification success rate has been found to be encouraging. The main feature of this implementation is that it is scalable. That is, any number of trained MLP units capable of identifying a certain parameter of damages can be integrated into the architecture and theoretically it will take almost the same time to identify various damage parameters irrespective of their numbers.

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.

In this paper, the concept of adaptive multiobjective genetic algorithm (AMOGA) is applied for the structure optimization of radial basis function network (RBFN). The problem of finding the number of hidden centers remains a critical issue in the design of RBFN. The number of basis function controls the complexity and generalization ability of the network. The most parsimonious network obtained from the pareto front is applied in one of the challenging research area of proteomics and computational biology: Protein superfamily classification. The problem deals with predicting the family membership of a newly discovered amino acid sequence. The modification to the earlier approach of multiobjective genetic algorithm (MOGA) is done based on the two key controlling parameters such as probability of crossover and probability of mutation. These values are adaptively varied based on the performance of the algorithm i.e., based on the percentage of total population present in the best nondomination level. Principal component analysis (PCA) is used for dimension reduction and significant features are extracted from long feature vector of amino acid sequences. Numerical simulation results illustrates the efficiency of our approach in terms of faster convergence, optimal architecture and high level of classification accuracy.