The Back-Propagation (BP) is the best known and widely used learning algorithm in training multiple neural network. A vast variety of improvements to BP algorithm have been proposed since ninety’s. in this paper, the effects of changing the number of hidden neurons and activation equation are investigated. According to the simulation results, the convergence speed have been improved and become much faster by the previous two modifications on the BP algorithm.

The aim of this research is to use robust technique by trimming, as the analysis of maximum likelihood (ML) often fails in the case of outliers in the studied phenomenon. Where the (MLE) will lose its advantages because of the bad influence caused by the Outliers. In order to address this problem, new statistical methods have been developed so as not to be affected by the outliers. These methods have robustness or resistance. Therefore, maximum trimmed likelihood: (MTL) is a good alternative to achieve more results. Acceptability and analogies, but weights can be used to increase the efficiency of the resulting capacities and to increase the strength of the estimate using the maximum weighted trimmed likelihood (MWTL). In order to perform t

In this communication, introduce the split Mersenne and Mersenne-Lucas hybrid quaternions, also obtaining generating functions and Binet formulas for these hybrid quaternions and investigating some properties among them.

Background: Klebsiella pneumoniae were considered as normal flora of skin, and intestine. It can cause damage to human lungs; the danger of this bacterium is related to exposure to the hospital surroundings. materials and methods: the detection of Klebsiella pneumoniae on morphological and biochemical tests and then assured with VITEK 2 system. Resistance to antibiotics was determined by Kirby-Baeur method. And genotyping of IMP-1 in isolates was done by PCR technique, then biofilm formation was identified by Micro titer plate method. Results: The present study included a collecting of 50 specimens from different clinical specimens, (blood 40%, urine 30%, sputum 20%, wound infection 10%); 10 isolates were identified as K

      A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs lengths and their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy. The optimization carried out is subjected to constraints that ensure a safe structure aga

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

In this paper, an approximate solution of nonlinear two points boundary variational problem is presented. Boubaker polynomials have been utilized to reduce these problems into quadratic programming problem. The convergence of this polynomial has been verified; also different numerical examples were given to show the applicability and validity of this method.

Afield experiment was conducted in the college of Agriculture labs Sulaimanya University in 2014 to estimate the genetic variance between 12 genotypes of maize. (SSR) technology was used depending on (PCR) technology. Twenty primers were used for the genetic variance analysis between the studied genotypes and 15 primers showed polymorphic results in repeated experiment caused to experinse 52 alleles which gave 307 bands of range 3.1 by different molecular sizes ranged between 80-1000 pair base the value of PIC reached to phi069 gave high value (0.8785), the range of genetic diversity for studied genotypes reached to (0.625) gave primers pairs phi069 hig value for genetic diversity (0.888). The value of total allele replication ranged at pri