Recursos de les assignatures per promoure la construcció del CDC a través del joc

Artificial neural networks are going through the change that happens once a concept departs the academic environment and is thrown into the harsher world of users who merely wish to get a job arranged. Several networks that are being designed recently are statistically quite exact but they still leave a defective impression with users who anticipate computers to solve their problems absolutely. These networks could be 85% to 90% accurate. Regrettably, a couple of applications tolerate that level of error.

While researchers continue to work on improving the accuracy of their

“creations,” some explorers are finding uses for the current technology.

In reviewing this state of the art, it is hard not to be overcome by the bright promises or tainted by the unachieved realities. Presently, neural networks are not the user interface which translates spoken works into instructions for a machine, but someday they will be. Someday, VCRs, home security systems, CD players, and word processors will simply be activated by voice. Touch screen and voice editing will replace the word processors of today while bringing spreadsheets and data bases to a level of usability pleasing to most everyone. But for now, neural networks are simply entering the marketplace in niches where their statistical accuracy is valuable as they await what will surely come.

Many of these niches indeed involve applications where answers are nebulous. Loan approval is one. Financial institutions make more money by having the lowest bad loan rate they can achieve. Systems that are

“90% accurate” might be an improvement over the current selection process. Indeed, some banks have proven that the failure rate on loans approved by neural networks is lower than those approved by some of their best traditional methods. Also, some credit card companies are using neural networks in their application screening process.

This latest method of looking into the future by examining past ex-periences has rendered its own independant problems. One of the major problems is to provide a reason behind the computer-generated answer, such as to why a particular loan application was denied. As mentioned throughout this chapter, the inner workings of neural networks are “black boxes.” The explanation of a neural net and its learning has been

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cult. To alleviate this difficult process, a lot of neural net tool developers have provided programs which explain which input through which node dominates the decision making process. From that information, experts in the application should be able to infer the reason that an exceptional piece of data is crucial.

Besides this filling of niches, neural network work is progressing in other more promising application areas. The following section of this chapter goes through some of these areas and briefly details the current work. This is done to help stimulate within the reader the various possi-bilities where neural networks might offer solutions, possipossi-bilities such as language processing, character recognition, image compression, pattern recognition, among others.

4.5.1 Language Processing

Human language users perform differently from their linguistic com-petence, that is from their knowledge of how to communicate correctly using their language. Natural language processing is an application re-inforced by the use of association of words and concepts, implemented as a neural network. A single neural network architecture is capable of processing a given sentence, and outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words, and the likelihood that the sentence makes sense grammatically and semantically using a language model.

Several researchers belonging to various universities are researching how a computer could be programmed to respond to spoken commands using the artificial neural networks. Natural language processing (NLP) has become the main-stream of research with neural networks (NNs), which are powerful parallel distributed learning/processing machines and they play a major role in several areas of NLP.

Presently, as reported by the academic journals, most of the hearing-capable neural networks are trained to only one talker. These one-talker, isolated-word recognizers can recognize only a few hundred words. But when there is a pause between each word, then the neural neworks can recognize more number of words.

A few investigators are touting even bigger potentialities, but due to the expected reward the true progress, and methods involved, are being closely held. The most highly touted, and demonstrated, speech-parsing system comes from the Apple Corporation. This network, according to an April 1992 Wall Street Journal article, is capable of recognizing almost any person’s speech through a limited vocabulary.

160 Computational Intelligence Paradigms 4.5.2 Character Recognition

The recognition of optical characters is known to be one of the ear-liest applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. Recognition of either handwritten or printed characters is a major area in which neu-ral networks are providing optimal solutions. Multilayer neuneu-ral networks are used to recognize characters which is a vital application in areas like banking, etc. The main issue in character recognition is the trade-off between cost and benefits such as accuracy and speed. Neural networks provide a method for combining independently trained characters to achieve higher performance at relatively low cost.

The greatest amount of recent research in the domain of character recognition is targeted at scanning oriental characters into a computer.

Presently, these characters require four or five keystrokes each. This com-plicated process stretches the task of identifying a page of text into hours of drudgery.

4.5.3 Data Compression

In neural networks, when the number of hidden units is less when compared to he input and output units, then the neurons of the mid-dle layer are capable of data compression. Researches have been proved that neural networks can do real-time compression and decompression of data. These networks are auto associative in that they can reduce eight bits of data in the input layer to three in the hidden layer and then reverse that process upon restructuring to eight bits again in the output layer. While compressing there is no loss in the information. Some of the major applications of data compression are multispectral lossless image compression pattern recognition lossy or lossless compression video com-pression handwritten numeral classification edge detection and magnetic resonance image compression.

4.5.4 Pattern Recognition

Pattern recognition is a very old application of neural networks and it has been studied in relation to many different (and mainly unrelated) ap-plications, such as classifying patterns by shape, identifying fingerprints, identifying tumors, handwriting recognition, face recognition, coin recog-nition, etc. The majority of these applications are concerned with prob-lems in pattern recognition, and make use of feed-forward network ar-chitectures such as the multi-layer perceptron and the radial basis func-tion network. According to the perspective of pattern recognifunc-tion, neural

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networks can be looked upon as an extension of the several traditional techniques which have been developed over several decades.

4.5.5 Signal Processing

The role of neural networks in signal processing is getting distributed, with practical applications in areas such as filtering, parameter esti-mation, signal detection, pattern identification, signal reconstruction, system identification, signal compression, and signal transmission. The signals concerned include audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and many others. The main characteristics of neural networks applied to signal processing are their asynchronous parallel and distributed processing, nonlinear dynamics, global interconnection of network elements, self-organization, and high-speed computational capability. Neural networks are capable of provid-ing effective means for resolvprovid-ing several problems encountered in sig-nal processing, especially, in nonlinear sigsig-nal processing, real-time sigsig-nal processing, adaptive signal processing, and blind signal processing.

4.5.6 Financial

Earlier financial experts used charts as the main source to navigate the large amount of financial data that was available. A few experts study the long term investments of companies while a few others try to anticipate the approaching economy or stock market in general. All these processes involved a large amount of risk in the work. In order to aid people in predicting particular markets,numerous computer programs are available. Traditionally, these programs are expensive, need complex programming, use surveys of financial experts to define the “game rules”, and are still limited in their ability to think like people. Still, the task is difficult even if the solution is obtained. In spite of the implications of the effective market hypothesis, many traders continue to make, buy, and sell decisions based on historical data. These decisions are made under the premise that patterns exist in that data, and that these patterns provide an indication of future movements. If such patterns exist, then it is possible in principle to apply automated pattern recognition techniques such as neural networks to the discovery of these patterns.

A neural network is a new kind of computing tool that is not limited by equations or rules. Neural networks function by finding correlations and patterns in the financial data provided by the user. These patterns become a part of the network during training. A separate network is needed for each problem you want to solve, but many networks follow the same basic format.

162 Computational Intelligence Paradigms

Summary

This chapter discusses the major class of neural network based on applications such as data classification and data association. Another network type described in this chapter is data conceptualization. Im-plementations of these networks using MATLAB Neural Network Tool box are also illustrated. The future of ANN holds even more promises.

Neural networks need faster hardware. They need to become part of hy-brid systems, which also utilize fuzzy logic and expert systems. It is then that these systems will be able to hear speech, read handwriting, and formulate actions. They will be able to become the intelligence behind robots that never tire nor become distracted. It is then that they will become the leading edge in an age of “intelligent” machines.

Review Questions

1. Explain the architecture and algorithm of LVQ.

2. What are the variants of LVQ? How do they differ from LVQ?

3. Mention the different types of counter propagation network.

4. What are in star and out star models?

5. Explain the application procedure of CPN.

6. Describe the architecture and algorithm of probabilistic neural net-work.

7. Explain the application procedure of Discrete Hopfield network.

8. Write a note on Boltzmann machine.

9. Derive an expression to determine the Hamming distance.

10. What are the different forms of BAM? How are BAM nets classi-fied?

11. Describe the major application areas of ANN.

12. Write a MATLAB program to implement competitive learning rule.

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13. Mention the functionality of supplemental and computational units of the ART network.

14. Assume the stored pattern to be [1 1 1 1 0 1 1 1 1]. Let there be mis-takes in the 2nd and 5th positions. Develop a Hopfield algorithm in MATLAB and determine the converged output.

15. What is the MATLAB Neural Network toolbox function used to find the weights in the layers of a LVQ network?

Chapter 5