Advantages and Limitations of Neural Networks

Advantages and Limitations of Neural Networks There are many advantages and limitations to neural network analysis and to discuss this subject properly we would have to look at each individual type of network, which isnt necessary for this general discussion. In reference to backpropagational networks however, there are some specific issues potential users should be aware of. Backpropagational neural networks (and many other types of networks) are in a sense the ultimate black boxes. Apart from defining the general archetecture of a network and perhaps initially seeding it with a random numbers, the user has no other role than to feed it input and watch it train and await the output. In fact, it has been said that with backpropagation, you almost dont know what youre doing. Some software freely available software packages (NevProp, bp, Mactivation) do allow the user to sample the networks progress at regular time intervals, but the learning itself progresses on its own. The final product of this activity is a trained network that provides no equations or coefficients defining a relationship (as in regression) beyond its own internal mathematics. The network IS the final equation of the relationship. Backpropagational networks also tend to be slower to train than other types of networks and sometimes require thousands of epochs. If run on a truly parallel computer system this issue is not really a problem, but if the BPNN is being simulated on a standard serial machine (i.e. a single SPARC, Mac or PC) training can take some time. This is because the machines CPU must compute the function of each node and connection separately, which can be problematic in very large networks with a large amount of data. However, the speed of most current machines is such that this is typically not much of an issue. The advantage of neural networks over conventional programming lies on their ability to solve problems that do not have an algorithmic solution or the available solution is too complex to be found. Neural networks are well suited to tackle problems that people are good at solving, like prediction and pattern recognition (Keller). Neural networks have been applied within the medical domain for clinical diagnosis (Baxt:95), image analysis and interpretation (Miller:92, Miller:93), signal analysis and interpretation, and drug development (Weinstein:92). The classification of the applications presented below is simplified, since most of the examples lie in more than one category (e.g. diagnosis and image interpretation; diagnosis and signal interpretation). Depending on the nature of the application and the strength of the internal data patterns you can generally expect a network to train quite well. This applies to problems where the relationships may be quite dynamic or non-linear. ANN s provide an analytical alternative to conventional techniques which are often limited by strict assumptions of normality, linearity, variable independence etc. Because an ANN can capture many kinds of relationships it allows the user to quickly and relatively easily model phenomena which otherwise may have been very difficult or imposible to explain otherwise. Future Enhancements Because gazing into the future is somewhat like gazing into a crystal ball, so it is better to quote some predictions. Each prediction rests on some sort of evidence or established trend which, with extrapolation, clearly takes us into a new realm. Prediction 1: Neural Networks will fascinate user-specific systems for education, information processing, and entertainment. Alternative ralities, produced by comprehensive environments, are attractive in terms of their potential for systems control, education, and entertainment. This is not just a far-out research trend, but is something which is becoming an increasing part of our daily existence, as witnessed by the growing interest in comprehensive entertainment centers in each home. This programming would require feedback from the user in order to be effective but simple and passive sensors (e.g fingertip sensors, gloves, or wristbands to sense pulse, blood pressure, skin ionisation, and so on), could provide effective feedback into a neural control system. This could be achieved, for example, with sensors that would detect pulse, blood pressure, skin ionisation, and other variables which the system could learn to correlate with a persons response state. Prediction 2: Neural networks, integrated with other artificial intelligence technologies, methods for direct culture of nervous tissue, and other exotic technologies such as genetic engineering, will allow us to develop radical and exotic life-forms whether man, machine, or hybrid. Prediction 3: Neural networks will allow us to explore new realms of human capability realms previously available only with extensive training and personal discipline. So a specific state of consciously induced neurophysiologically observable awareness is necessary in order to facilitate a man machine system interface. Recommendations The major issues of concern today are the scalability problem, testing, verification, and integration of neural network systems into the modern environment. Neural network programs sometimes become unstable when applied to larger problems. The defence, nuclear and space industries are concerned about the issue of testing and verification. The mathematical theories used to guarantee the performance of an applied neural network are still under development. The solution for the time being may be to train and test these intelligent systems much as we do for humans. Also there are some more practical problems like: the operational problem encountered when attempting to simulate the parallelism of neural networks. Since the majority of neural networks are simulated on sequential machines, giving rise to a very rapid increase in processing time requirements as size of the problem expands. Solution: implement neural networks directly in hardware, but these need a lot of development still. ÂÂ ¢ instability to explain any results that they obtain. Networks function as black boxes whose rules of operation are completely unknown. There are many advantages and limitations to neural network analysis and to discuss this subject properly we would have to look at each individual type of network, which isnt necessary for this general discussion. In reference to backpropagational networks however, there are some specific issues potential users should be aware of. ÂÂ ¢ Backpropagational neural networks (and many other types of networks) are in a sense the ultimate black boxes. Apart from defining the general archetecture of a network and perhaps initially seeding it with a random numbers, the user has no other role than to feed it input and watch it train and await the output. In fact, it has been said that with backpropagation, you almost dont know what youre doing. Some software freely available software packages (NevProp, bp, Mactivation) do allow the user to sample the networks progress at regular time intervals, but the learning itself progresses on its own. The final product of this activity is a trained network that provides no equations or coefficients defining a relationship (as in regression) beyond its own internal mathematics. The network IS the final equation of the relationship. ÂÂ ¢ Backpropagational networks also tend to be slower to train than other types of networks and sometimes require thousands of epochs. If run on a truly parallel computer system this issue is not really a problem, but if the BPNN is being simulated on a standard serial machine (i.e. a single SPARC, Mac or PC) training can take some time. This is because the machines CPU must compute the function of each node and connection separately, which can be problematic in very large networks with a large amount of data. However, the speed of most current machines is such that this is typically not much of an issue. Conclusion In this paper, we have presented a system for recognizing handwritten English characters. An experimental result shows that backpropagation network yields good recognition accuracy of 85%. We have demonstrated the application of MLP network to the handwritten character recognition problem. The skeletonized and normalized binary pixels of these characters were used as the inputs of the MLP network. In our further research work, we would like to improve the recognition accuracy of network for character recognition by using more training samples written by one person and by using a good feature extraction system. The training time may be reduced by using a good feature extraction technique and instead of using global input, we may use the feature input along with other neural network classifier. The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture. Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain. Perhaps the most exciting aspect of neural networks is the possibility that some day conscious networks might be produced. There are a number of scientists arguing that consciousness is a mechanical property and that conscious neural networks are a realistic possibility. Finally, I would like to state that even though neural networks have a huge potential we will only get the best of them when they are integrated with computing, AI, fuzzy logic and related subjects

Mike and Marty Essay

How would you rate Mike and Marty on the Big Five personality traits? I think Mike and Marty are complete opposites from each other. On the personality traits I rate them for the same traits but opposite sides. For example both have neuroticism, but Marty is stable, calm but insecure when Mike is tense, anxious but secure. I would also rate them both for conscientiousness Marty being careful, disciplined and organized but Mike being the opposite, careless, impulsive and disorganized. For extraversion, I think Marty is quiet, sober and retiring when Mike is mike is definitely talkative, fun-loving and sociable. Even though they have the same traits, they are opposites from each other. Which of the two brothers seems more likely to be achieving self-actualization? Explain. I think Marty is the one that is more likely to achieve self-actualization. He seems to know his goals and is achieving one after another. He is very responsible and is going a secure path, full of achievements. He has a fulfilling career and a family, something most people are striving for. He will most likely feel like he has succeeded in life, which is an important step on the way to self-actualization. Mike on the other hand seems to live for the day, not being much concerned about tomorrow. He is very irresponsible with his actions, which one day will catch up with him. I think he is not going to be able to achieve self-actualization because he won’t feel like he has succeeded in life or that he has done everything right and well. Do Mike and Marty appear to have different levels of intelligence, or do they show intelligence in different ways? Please explain. I don’t think you can tell who is more intelligent here. Marty is definitely book-smarter than Mike, but Mike is social and has a different kind of intelligence. I am sure that Marty is more logical-mathematical intelligent than Mike, but Mike is more interpersonal intelligent than Marty. Marty shows his intelligence in his school past and his career and Mike is showing his intelligence in being a social person. I believe Mike has more friends than Marty because he is more outgoing and confident. Both are intelligent but in different ways, you can tell they are expressing their intelligence differently with their life choices.

Whats the Relationship Between Communication and Identity

Communication and identity, many wonder if these words come together? Or wether or not they can work in accord ? Most people would testify different, but in all actuality they can and do more often than one expect, depending on which channels you use and in which context, the way you communicate along with your identity will undergo some modifications, and that without forgetting to include what a big role your gender, social, and cultural identities plays in that as well .As a source to reinforce my theory in this paper I will discuss what I've learned but not limited from Chapter one and two of Communication in a Changing World by Bethami A. Dobkin & Roger C. Pace but also will add one or two real life examples about the relationship between communication and identity, and also has a conclusion this paper will discuss the differences in when I communicate with gender, cultural and social identities in both a face-to-face and online environments.By definition to communicate is to cr eate and share meaning through the use of symbol (The words, images, gestures, and expressions that we use to represent our thoughts, ideas, beliefs, and feelings. ) through a distinctive process, whereas identity is the conception of yourself as a member of group or category (Dobkin & Pace, 2006). The relationship between communication and identity is normal when communicating is usually from a social standpoint. The things we mostly communicate about our identity are either but not limited to how we feel or the way we would like to come off to others.Communication is another form of representation. A lot of the times, we associate ourselves with either who we are or who we want to be. It is also what we go through or what we envision that determine the way we respond or what we say to others. For example, sex can very well determine your occupation and age can determine your hobbies or recreations. Sexual orientation can determine who your friends are and the places you will hang out and ethnicity can determine your opinions or the ethnicity of your peers.

The Role of Language in Shakespeares Play The Tempest Essay

The Role of Language in Shakespeares Play The Tempest 1 The role of language in Shakespeare’s play â€Å"The Tempest† is quite significant. To Miranda and Prospero the use of language is a means to knowing oneself. Caliban does not view language in the same light. Prospero taught Caliban to speak, but instead of creating the feeling of empowerment from language, Caliban reacts in insurrectionary manner. Language reminds him how different he is from Miranda and Prospero, and also how they have changed him. It also reminds him of how he was when he wasn’t a slave. He resents Prospero for â€Å"Civilising† him, because in doing so he took away his freedom. Language and knowledge is the key to power on the island. Prospero is a well educated man,†¦show more content†¦The red plague rid you For learning me your language! (I.ii: 363-65) is quite confusing. Why would he want to curse the man who taught him how to speak? There are a number of reasons for this. Caliban can now comprehend his diverseness. He also feels trapped by the language because he sees the ability to speak and understand Prospero’s language as the instrument which took away his freedom. Also language symbolizes civility. He did not know before Prospero and Miranda’s arrival of class and race differences. Through â€Å"culture† he has learned of discrimination, and he is being discriminated against. This makes him a pariah on his own island. The meaning of Caliban’s words is that, he explains that he resents being taught to speak, and that he can only see one advantage for him to be able to do so, and that is the ability to curse, because with that ability he can curse Prospero whom he begrudges the most. 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