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Explanation of My Report in CMSC 411

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Explanation of My Report in CMSC 411

Explanation of My Report in CMSC 411

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  • 1. (This is the explanation of the report entitled Knowledge Based Systems) On Figure 16.10 shows how a Prototyping is being included as a part of the Development of an Expert System based on the diagram you can see the steps or phases in able for a researcher to conduct a research or to develop a system. Sample Expert System In 1980, the Expert System began its activity in business, as a matter of fact, two professors developed a sample of such financial Expert System a Credit Approval System developed by Professor VenKat Srinivasan and Professor Yong H. Kim of the University of Cincinati. This two professors interviewed credit managers and they also observed how a credit analysis make credit decisions. The Knowledge Base The knowledge base of the expert system consists of two components; (1) rule that reflect the credit approval logic, and (2) a mathematical model that determines the credit limit. When one of the rules is fired, the customer is given a rating of Excellent in the corresponding category. The User Interface On Figure 16.12 shows a sample set of rules that is used to determine the customer’s financial strength. When one of the rules is fired, the customer is given a rating of Excellent in the corresponding category. Advantage and Disadvantage of Expert System to Managers  The Advantages of Expert Systems to Managers. Managers solve problems so that the firm can meet its objectives. During the course of solving a problem, when the manager is making multiple decisions, an expert system will be beneficial because it can improve their decision-making that will be very useful for them and also for the firm. The improvement comes from being able to: 1. Consider More Alternatives. Because a manager is a decision maker on the firm or business, an expert system can help the manager to create or to developed more alternatives solutions for the problem that is being encountered by the firm. 2. Apply a Higher Level of Logic. An expert system also helps a manager to apply the same logic as that of leading expert in the field. 3. Devote More Time of Evaluating Decision Result. One of the benefits of the expert system is that it can give advice for the manager
  • 2. in a short period of time, because of this manager will be able to see what will be the results of their action or the results of the solution that they will choose to solve a certain problem. 4. Make More Consistent Decisions. Because of the expert system managers can develop more appropriate. Decisions and they can be able to determine what will be the best solution for the problem because of this the firm can meet its main objectives. The Disadvantages of Expert Systems There are two disadvantages of an expert system. First, it cannot handle inconsistent knowledge and second, it cannot apply the judgments and intuition that are important ingredients when solving semi-structured or unstructured problems. Neural Networks A neural network is a mathematical model of the human brain that simulates the way that neurons interact to process data and learn from the experience. Biological Comparisons A neural network has been inspired by the physical design of the human brain. The neuron is the component of the brain that provides information processing capability. Information processing capability has 3 basic regions.  Dendrites - specialized in the input of electrochemical signals. - in terms of computer schematic, dendrites is equivalent to the input device of the computer.  Soma - is the one who is responsible to process the signals. - equivalent to the Central Processing Unit (CPU) of a Computer.  Axon - provides output paths for the processed signals. - equivalent to the Output Device of the Computer. - A Parallel Distributed Processing (PDP) allows task to be broken down into a great number of (multitude) of subtasks that are performed concurrently. The Evolution of Artificial Neural Systems The modeling of human learning system started million of years ago and one of those learning system was developed by the Chinese artisans in as early as 200 B.C. However, most researchers consider the development done by Warren McCullock and Walter Pitts in 1930’s as the real starting point. To developed the device McCullock and Pitts used a method or a universal learning rules for adjusting the weights in the neuron function.
  • 3. The Artificial Neural System The Artificial Neural System (ANS) is not an exact representation of the human brain, but it has the capability of learning, generalization, abstraction, and even intuition. An ANS is made up of a series of very simple artificial neuron structure or neurodes (perceptions). The neuron structures or neurodes are often referred to as perceptions because of the influence of the research of Frank Rosenblatt. The Multi-Layer Perception A simple neurons that are combined to form a multi-layer ANS. This multi- layer perception is a feed forward network meaning that the flow of data moves only in a single direction, from the input layer to the output layer. However, the hidden layers permit an interaction between inputs and outputs that facilitates their training. Network Training A neural net (neural network) is not programmed in the traditional sense. As you can see in Figure 16.15 (A Single Artificial Neuron) all the inputs (Y1-Yn-1) will compress and will show only one output this output are the sum (Y) of all the inputs, based on the diagram you can easily see the flow of data. Putting the Artificial Neural System in Perspective The ANS can be distinguish from an expert system applications because an ANS has the ability to learn based on adaptation just like the human brain. An Expert System are programmed to make inferences based on data that describes the problem environment. The ANS, on the other hand able to adjust the modal weights in response to the inputs and possibly to the desired outputs. On Figure 16.16 (The Multi-Layer Perception). You can see how input layer are related to the other inputs or how hidden layer are related to the output layer and vise versa.