Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

http://masou-keshtkar.persiangig.com/document/sdarticle.pdf

GSA: A Gravitational Search Algorithm

Introduction to Optimization The Binary Genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.

https://cdn.preterhuman.net/texts/science_and_technology/nature_and_biology/Bioinformatics/Practical%20Genetic%20Algorithms%20-%20Randy%20L.%20Haupt,%20Sue%20Ellen%20Haupt.pdf

Practical Genetic Algorithms

Phased Arrays in Radar and Communication Systems. Pattern Characteristics and Synthesis of Linear and Planar Arrays. Patterns of Nonplanar Arrays. Elements, Transmission Lines, and Feed Architectures for Phased Arrays. Summary of Element Pattern and Mutual Impedance Effects. Array Error Effects. Special Array Feeds for Limited Field of View and Wideband Arrays.

Phased Array Antenna Handbook

Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of apt array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing.

/pdf/application-of-antenna-arrays-to-mobile-communications-ii-52fds1c81j.pdf

Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations

This paper describes an adaptive nulling system for reflector antennas that uses a genetic algorithm to mechanically adjust scattering elements in order to place nulls in the sidelobes of cylindrical parabolic reflector antennas.

Adaptive nulling with a reflector antenna using movable scattering elements

A wide bandwidth waveform received by a large phased array antenna experiences dispersion/beam squint when using phase shifters to steer the beam instead of true time delay (TTD) units. An analysis is performed on the losses incurred by a digitally modulated signal due to phase steering.

Degradation in theoretical phase shift keying waveforms due to signal dispersion in a large communications phased array

A genetic algorithm (GA) finds complex weights that yield low-sidelobe levels for spherical arrays. The first example is a spherical–planar array with an initial high sidelobe of −13 dB, and which has an optimized pattern with a maximum sidelobe level of −27 dB. The second example is a spherical–circular array with an initial high sidelobe of −16 dB, and which has an optimized pattern with a maximum sidelobe level of −34 dB. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 32: 412–414, 2002.

Low-sidelobe pattern synthesis of spherical arrays using a genetic algorithm

A system for reducing sidelobes of phased array antenna systems is performed by tapering the current amplitide of edge elements in the array while maintaining a full uniform current to the center elements in the array. Tapering may be adjusted with variable impedance devices until optimum sidelobe reduction is achieved.

Low sidelobe array by amplitude edge tapering the edge elements

Tapering the resistivity on the surface of an object modifies the scattering patterns of that object. For instance, gradually tapering the resistivity on a strip, half-plane, or an antenna ground plane reduces the edge effects of that surface. Greater control over the sidelobe response of the scattering pattern of a strip is possible by relating the resistive taper to a low sidelobe taper via physical optics. The idea of relating antenna aperture tapers to scattering patterns is extended to placing nulls in the sidelobes of scattering patterns of strips. A resistive taper for placing nulls is found by solving the scattering integral equations of a resistive strip for the resistivity, then substituting the desired nulling current density taper into the appropriate equation and solving for the resistive taper. This method is capable of placing multiple nulls in the bistatic scattering pattern or a single null in the backscattering pattern. >

Randy L. Haupt

Papers

Adaptive nulling with a reflector antenna using movable scattering elements

Degradation in theoretical phase shift keying waveforms due to signal dispersion in a large communications phased array

Low-sidelobe pattern synthesis of spherical arrays using a genetic algorithm

Low sidelobe array by amplitude edge tapering the edge elements

Resistive tapers that place nulls in the scattering patterns of strips