The most important thing to realize about fuzzy logical reasoning is that it is a superset of standard Boolean logic, i. e., if the fuzzy values are kept at their extremes of 1 (completely true) and 0 (completely false), standard logical operations hold. In fuzzy logic, however, the truth of any statement is a matter of degree. The input values can be real numbers between 0 and 1. It should be noted that the results of the statement A AND B, where A and B are limited to the range (0, 1) can be resolved by using min (A, B). Similarly, an OR operation can be replaced with the max function so that A OR B becomes equivalent to max (A, B), and the operation NOT A is equivalent to the operation 1 – A...Read More
Fuzzy logic is a logical system, which is an extension of multi-valued logic. Additionally, fuzzy logic is almost synonymous with the theory of fuzzy sets, a theory that relates to classes of objects without sharp boundaries in which membership is a matter of degree. Fuzzy logic is all about the relative importance of precision, i. e., how important it is to be exactly right when a rough answer will work. Fuzzy inference systems have been successfully applied in fields such as automatic control, data classification, decision analysis, expert systems, and computer vision...Read More
Genetic algorithms were used by the author in a number of optimization problems: the optimal design of flat-plate solar collectors (Kalogirou, 2003c), predicting the optimal sizing coefficient of photovoltaic supply systems (Mellit and Kalogirou, 2006a), and the optimum selection of the fenestration openings in buildings (Kalogirou, 2007). They have also been used to optimize solar energy systems, in combination with TRNSYS and ANNs (Kalogirou, 2004a). In this, the system is modeled using the TRNSYS computer program and the climatic conditions of Cyprus...Read More
The genetic algorithm (GA) is a model of machine learning that derives its behavior from a representation of the processes of evolution in nature. This is done by the creation, within a machine or computer, of a population of individuals represented by chromosomes. Essentially, these are a set of character strings that are analogous to the chromosomes in the DNA of human beings. The individuals in the population then go through a process of evolution.
It should be noted that evolution as occurring in nature or elsewhere is not a purposive or directed process, i. e., no evidence supports the assertion that the goal of evolution is to produce humankind. Indeed, the processes of nature seem to end with different individuals competing for resources in the environment...Read More
One type of neural network that is very suitable for modeling is the group method of data handling (GMDH) neural network. The group method of data handling technique was invented by A. G. Ivakhenko, from the Institute of Cybernetics,
Ukrainian Academy of Sciences (Ivakhenko, 1968, 1971), but enhanced by others (Farlow, 1984). This technique is also known as polynomial networks. Ivakhenko developed the GMDH technique to build more accurate predictive models of fish populations in rivers and oceans. The GMDH technique worked well for modeling fisheries and many other modeling applications (Hecht-Nielsen, 1991). The GMDH is a feature-based mapping network.
The GMDH technique works by building successive layers, with links that are simple polynomial terms...Read More
Another type of architecture is general regression neural networks (GRNNs), which are known for their ability to train quickly on sparse data sets. In numerous tests, it was found that a GRNN responds much better than back-propagation to many types of problems, although this is not a rule. It is especially useful for continuous function approximation. A GRNN can have multidimensional input, and it will fit multidimensional surfaces through data. GRNNs work by measuring how far a given sample pattern is from patterns in the training set in N dimensional space, where N is the number of inputs in the problem. The Euclidean distance is usually adopted.
A GRNN is a four-layer feed-forward neural network based on the nonlinear regression theory, consisting of the input layer, the patte...Read More
Architectures in the back-propagation category include standard networks, recurrent, feed forward with multiple hidden slabs, and jump connection networks. Back-propagation networks are known for their ability to generalize well on a wide variety of problems. They are a supervised type of network, i. e., trained with both inputs and outputs. Back-propagation networks are used in a large number of working applications, since they tend to generalize well.
The first category of neural network architectures is the one where each layer is connected to the immediately previous layer (see Figure 11.17). Generally, three layers (input, hidden, and output) are sufficient for the majority of problems to be handled...Read More
Though most scholars are concerned with the techniques to define artificial neural network architecture, practitioners want to apply the ANN architecture to the model and obtain quick results. The term neural network architecture refers to the arrangement of neurons into layers and the connection patterns between layers, activation functions, and learning methods. The neural network model and the architecture of a neural network determine how a network transforms its input into an output. This transformation is, in fact, a computation. Often, the success depends on a clear understanding of the problem, regardless of the network architecture. However, in determining which neural network architecture provides the best prediction, it is necessary to build a good model...Read More