This study has reached to that ANN (5-9-1) (five neurons in input
layer_nine neurons in hidden layer _ one neuron in output layer) is the
optimum artificial network that hybrid system has reached to it with
mean squared error equals (1*10^-4) (0.7
m3/sec), where this software
has summed up millions of experiments in one step and in limited time, it
has also given a zero value of a number of network connections, such as
some connections related of relative humidity input because of the lake
of impact this parameter on the runoff when other parameters are
avaliable.
This study recommend to use this technique in forecasting of
evaporation and other climatic elements.
After selecting real seismic records it is necessary to scale these records to match the intensity of the earthquake expected for the site. Generally, scaling can be made by ground motions uniform scaling in time domain which is simply scaled up or d
own the ground motions uniformly to best match (in average) the target spectrum within a period range of interest. It’s an engineer’s job to find the best scaling factors to best match the target spectrum, which is a complex task, so we employed the Genetic Algorithm (GA) in finding those scaling factors to achieve the best results.
Genetic Algorithms (GAs) are probably the best-known types of artificial evolution search methods based on natural selection and mechanisms of population genetics. These algorithms are often applied to large, complex problems that are non-linear with multiple local optima.
The power of the genetic algorithms is inherent in its capability to adapt. In natural systems, species adapt to the environment through successive interactions and generations subject to the environment. After several consecutive generations, only those species that can adapt well to the environment survive and the rest disappear. In mathematical terms, individuals are analogous to problem variables and environment is the stated problem. The final generation of the variable strings that can adapt to the problem is the solution.
In this study, basic methodologies of the GA and the scaling procedures are summarized and the scaling criteria of real time history records to satisfy the Syrian design code are discussed. The traditional time domain scaling procedures and the scaling procedures using GA are used to scale a number of the available real records to match the Syrian design spectra. The resulting time histories of the procedures are investigated and compared in terms of meeting criteria.
One of the principal applications of fuzzy logic is in control system design. Fuzzy logic controllers (FLC) can be used to control
systems where the use of conventional control techniques may be Problematic. The tuning of fuzzy controllers has tende
d to rely on human expert knowledge, but where the number of rules and fuzzy sets is large. The Problem of generation desirable fuzzy rule is very important in the development of fuzzy systems. The purpose of this paper is to present a generation method of fuzzy control rules by learning from examples using genetic algorithms (GA). We propose real coded genetic algorithms (RCGA) for learning fuzzy rules, and an iterative process for obtaining set of rules which covers the examples set with a covering value previously defined.
Scientists nowadays increased their interest in artificial intelligence because
of the rapid development in the modern age. This development increased
the complexity of systems in order to consent society rapid needs in getting
systems of better r
eliability and high performance.
Artificial Intelligence solved many difficult and sticky problems.
We are going to define the genetic algorithms(GA) which is one of the
artificial intelligence branch because of its ability to solve many complex
problems in Different scientific aspects either in computer science[8] or
operational research and image processing[7] or social science[9].
In this essay, we used GA to find the maximum value of continuous function
within a limited rang to study the effect of some of the most important GA
parameters on the performance and accuracy of the results. We noticed the
effects of probability of mutation, population size and the number of the
repeated operations on the results accuracy and execution time in choosing
the Roulette Wheel Selection procedure. After that, we compare
between the Roulette Wheel procedure and the Elitism Selection procedure.
