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Artificial Neural Networks common app essay help Social Science homework help

The developments in Artificial Intelligence (AI) appear promising, but when applied to real world intelligent task such as in speech, vision and natural language processing, the AI techniques show their inadequacies and ‘brittleness’ in the sense that they become highly task specific. The computing models inspired by biological neural networks can provide new directions to solve problems arising in natural tasks. The purpose of this paper is to discuss the Characteristics and Applications of Artificial Neural Networks.

In Characteristics of Neural Networks, we will discuss about the Features of Biological Neural Networks and Performance comparison of computer and Biological Neural Networks. In applications we discuss about Direct applications which include Pattern classification, Associative memories, Optimization and Control applications and Application Areas. At last we have conclusion and Bibliography

Some attractive features of the biological neural network that made it superior to even the most sophisticated Artificial Intelligence computer system for pattern recognition tasks are the following: • Robustness and fault tolerance: The decay of nerve cells does not seem to affect the performance significantly. • Flexibility: The network automatically adjusts to a new environment without using any preprogrammed instructions.

Speed: – Neural networks are slow in processing information. For the most advanced computers the cycle time corresponding to execution of one step of a program in the central processing unit is in the range of a few nanoseconds. The cycle time corresponding to neural event prompted by an external stimulus occurs in milliseconds range. Thus the computer processes information nearly a million times faster. Processing: – Neural networks can perform massively parallel operations. Most programs have large number of instructions, and they operate in a sequential mode one instruction after another on a conventional computer.

On the other hand, the brain operates with massively parallel operations, each of them having comparatively fewer steps. This explains the superior performance of human information processing for certain tasks, despite being several orders of magnitude slower compared to computer processing of information. Size And Complexity: – Neural networks have large number of computing elements , and the corresponding is not restricted to with in neurons. The number of neurons in a brain is estimated to be about 10^11 and the total number interconnections to be around 10^15.

It is this size and complexity of connections that may be giving the brain the power of performing complex pattern recognition tasks, which we are unable to realize on a computer. The complexity of brain is further compounded by the fact that computing takes place not only inside the cell body, or soma, but also outside in the dendrites and synapses. Storage: – Neural networks store information in the strengths of the interconnections. In a computer, information is stored in the memory, which is addressed by its location. Any new information in the same location destroys the old information.

In contrast, in a neural network new information is added by adjusting the interconnecting strengths, with out destroying the old information. Thus information in the brain is adaptable, where as in the computer it is strictly replaceable. Fault Tolerance: – Neural networks exhibit fault tolerance since the information is distributed in the connections throughout the network. Even if a few connections are snapped or a few neurons are not functioning, the information is still preserved due to the distributed nature of the encoded information.

In contrast, computers are inherently not fault tolerant, in the sense that information corrupted in the memory cannot be retrieved. Control Mechanism: – There is no central control for processing information in the brain. In a computer there is a control unit, which monitors all the activities of computing. In a neural network each neuron acts based on the information locally available, and transmits its output to the neurons connected to it. Thus there is no specific control mechanism external to the computing task.

Currently, fuzzy logic concepts are being used to enhance the capability of the neural networks to deal with real world problems such as in speech, image processing, natural language processing and decision-making. ARTIFICIAL NEURAL NETWORKS: TERMINOLOGY Processing Unit: We can consider an artificial neural network (ANN) as a highly simplified model of a structure of the biological neural network. ANN consists of interconnected processing units. The general model of a processing unit consists of summing part followed by an output part.

The summing part receives N input values, weights each value, and computes a weighted sum. The weighted sum is called the activation value. The output part produces a signal from the activation value. The sign of the weight for each input determines whether the input is excitatory (positive weight) or inhibitory (negative weight). The inputs could be discrete or continuous data values, and likewise the outputs also could be discrete or continuous. The input and output could also be deterministic or stochastic or fuzzy. Interconnections:

In an artificial neural network several processing units are interconnected according to some topology to accomplish a pattern recognition task. Therefore the inputs to a processing unit may come from the outputs of other processing units, and/or from external sources. The output of each unit may be given to several units including it. Operations: In operation, each unit of an ANN receives inputs from other connected units and/or from an external source. A weighted sum of the inputs is computed at a given instant of time. The activation value determines the actual output from the output function unit, i. . , the output state of the unit. The output values and other external inputs in turn determine the activation and output states of the other units.

In implementation, there are several options available for both activation and synaptic dynamics. In particular, the updating for the output states of all the units could be performed synchronously. For each unit, the output state can be determined from the activation value either deterministically or stochastically. In the applications two different situations exist: The known neural networks concepts and models are directly applicable. • There appears to be potential for using the neural networks ideas, but it is not yet clear how to formulate the real world problems to evolve suitable neural network architecture. Apart from the attempts to apply some existing models for real world problems, several fundamental issues are also being addressed to understand the basic operations and dynamics of the biological neural network in order to derive suitable models of artificial neural networks.

Pattern classification: Pattern classification is the most direct among all applications of neural networks. Infact, neural networks became very popular because of the ability of a multi layer feed forward neural network to form complex decision regions in the pattern space for classifications. Many pattern recognition problems, especially character or other symbol recognition and vowel recognition, have been implemented using a multi layer neural network.

Note, however, that these networks are not directly applicable for situations where the patterns are deformed or modified due to transformations such as translation, rotation and scale change, although some of them may work well even with large additive uncorrelated noise in the data. Direct applications are successful, if the data is directly presentable to the classification network. Three such cases are considered for detailed discussion in this section.

Explain the historical context in which the Mexican folk songs or corridors were written.

Explain the historical context in which the Mexican folk songs or corridors were written..

Drawing specific examples from the assigned reading, write a 750-1200 word paper explaining the historical context in which the Mexican folk songs or corridos were written. Analyze the content of the songs by identifying the major themes in the songs. Conclude with a discussion of what role you think the songs played in the Mexican American community in the 1920s. Academic Level : Bachelor Paper details Prepare To prepare for this assignment, carefully read Brown and Shannon, Going to the Source, vol. 2 Chapter 7 “Singing of Struggle: Mexican Workers’ Folk Songs from the American Southwest.” Complete the source table to help you analyze the songs before you write. If you need additional sources, use the resources listed under “To Find Out More” at the end of the chapter. IMPORTANT: You may not use any other outside sources except those specifically listed in the chapter. Listen To listen to recordings of corridos from the 1930s, click on this link (opens in a new window):John and Ruby Lomax Collection; Once in the collection, search “corridos.” https://www.loc.gov/collections/john-and-ruby-lomax/about-this-collection/ Paper Format double spaced 12 pt font 1 inch margins Chicago Style citations: footnotes/endnotes (see Purdue OWL site for info/examples) Chicago Style bibliography word count at bottom of last page

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