Genetic Algorithm

A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).

Genetic algorithms are implemented as a computer simulation in which a population of abstract representations (called chromosomes or the genotype or the genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached.

A biological system can be exceedingly small. Many of the cells are very tiny, but they are very active; they manufacture various substances; they walk around; they wiggle; and they do all kinds of marvelous things – all on a very small scale. Also, they store information. Consider the possibility that we too can make a thing very small which does what we want – that we can manufacture an object that maneuvers at that level.
(From the talk “There’s Plenty of Room at the Bottom”, delivered by Richard P. Feynman at the annual meeting of the American Physical Society at the California Institute of Technology, Pasadena, CA, on December 29, 1959.)

INTRODUCTION TO TURBOMACHINERY

A turbomachine is a device in which energy transfer occurs between a flowing fluid and a rotating element due to dynamic action, and results in a change in pressure and momentum of the fluid. Mechanical energy transfer occurs inside or outside of the turbomachine, usually in a steady-flow process. Turbomachines include all those machines that produce power, such as turbines, as well as those types that produce a head or pressure, such as centrifugal pumps and compressors. The turbomachine extracts energy from or imparts energy to a continuously moving stream of fluid. However in a positive displacement machine, it is intermittent. The turbomachine as described above covers a wide range of machines, such as gas turbines, steam turbines, centrifugal pumps, centrifugal and axial flow compressors, windmills, water wheels, and hydraulic turbines.

Precision is not truth.
Henri E. B. Matisse, 1869–1954
Impressionist painter

What is Mechatronics?
Mechatronics is a natural stage in the evolutionary process of modern engineering design. The development of the computer, and then the microcomputer, embedded computers, and associated information technologies and software advances, made mechatronics an imperative in the latter part of the twentieth century. Standing at the threshold of the twenty-first century, with expected advances in integrated bioelectro- mechanical systems, quantum computers, nano- and pico-systems, and other unforeseen developments, the future of mechatronics is full of potential and bright possibilities.

The definition of mechatronics has evolved since the original definition by the Yasakawa Electric Company. In trademark application documents, Yasakawa defined mechatronics in this way:
The word, mechatronics, is composed of “mecha” from mechanism and the “tronics” from electronics. In other words, technologies and developed products will be incorporating electronics more and more into mechanisms, intimately and organically, and making it impossible to tell where one ends and the other begins.

As in Book "MECHATRONICS AN INTRODUCTION" written by "Robert H. Bishop" Published by "CRC Press"

The task of the anaesthetist is to control the continuum between consciousness and unconsciousness, pain and analgesia, muscle activity and relaxation—inhibition of activation and enhancement of inhibition. During an operation, an anaesthetized patient is part of a ‘feedback circuit’ (Figure 1). Changes in variables such as blood pressure and respiratory rate are monitored and stability is restored by adjustments to ventilation and drug dosage. The decision-maker and controller in this loop is the anaesthetist, who will make an individual judgment on how best to respond to, say, low blood pressure, tachypnoea or a decreasing oxygen saturation. Computer programs employing ‘fuzzy logic’ are intended to imitate human thought processes in these complex circumstances but to function at greater speed. A simple computerized system might be based on the rule ‘if X then do Y’. The drawback of such programs is that a large number of rules are needed to deal with every possible situation. In addition, if two or more indices are being measured the rule then becomes ‘if X and Y, then Z’ and the number of rules multiplies vastly. Fuzzy logic works by drastically reducing the number of rules and using proportionate amounts of each rule; and it can also ‘learn’ by assessing responses to changes in output. It thus opens the way to automation in circumstances that would be difficult or impossible to model with simple linear mathematics.

"Fuzzy logic and decision-making in anaesthetics" by " Paul Grant & Ole Naesh" in "
J O U R N A L O F T H E R O Y A L S O C I E T Y O F M E D I C I N E V o l u m e 9 8 J a n u a r y 2 0 0 5"

Shows the application of the Fuzzy Logic in different areas.

Fuzzy Logic

Fuzzy logic was developed by Lotfi A. Zadeh in the 1960s in order to provide mathematical rules and functions which permitted natural language queries. Fuzzy logic provides a means of calculating intermediate values between absolute true and absolute false with resulting values ranging between 0.0 and 1.0. With fuzzy logic, it is possible to calculate the degree to which an item is a member. For example, if a person is .83 of tallness, they are " rather tall. " Fuzzy logic calculates the shades of gray between black/white and true/false. Fuzzy logic is a super set of conventional (or Boolean) logic and contains similarities and differences with Boolean logic. Fuzzy logic is similar to Boolean logic, in that Boolean logic results are returned by fuzzy logic operations when all fuzzy memberships are restricted to 0 and 1. Fuzzy logic differs from Boolean logic in that it is permissive of natural language queries and is more like human thinking; it is based on degrees of truth.
The word “fuzzy” is perhaps no longer fuzzy to many engineers today. Fuzzy systems and fuzzy control theories as an emerging technology targeting industrial applications have added a promising new dimension to the existing domain of conventional control systems engineering. It is now a common belief that when a complex physical system does not provide a set of differential or difference equations as a precise or reasonably accurate mathematical model, particularly when the system description requires certain human experience in linguistic terms, fuzzy systems and fuzzy control theories have some salient features and distinguishing merits over many other approaches. Fuzzy control methods and algorithms, including many specialized software and hardware available on the market today, may be classified as one type of intelligent control. This is because fuzzy systems modeling, analysis, and control incorporate a certain amount of human knowledge into its components (fuzzy sets, fuzzy logic, and fuzzy rule base). Using human expertise in system modeling and controller design is not only advantageous but often necessary.
(For more detail go to web site: http://www.dementia.org/~julied/logic/)