Concept came from biological background
Very important computational perspective.
Nervous system
inspired the development of ANN
Immune system
Led to the emergence of AIS as a novel computational intelligence paradigm.
Artificial immune systems developed using ideas, theories, and components, extracted from the immune system.
WHAT IMMUNE SYSTEM DOES?
All living organisms are capable of presenting some type of defense against foreign attack.
Main task
Search for malfunctioning cells from their own body (e.g., cancer and tumour cells), and foreign disease causing elements (e.g., viruses and bacteria)
Made up by great variety of molecules, cells, and organs spread all over the body
NEGATIVE SELECTION
After T-cells are generated, they migrate into the thymus where they mature. During this maturation, all T-cells that recognize self-antigens are excluded from population of T-cells; a process termed negative selection.
Negative selection presents an alternative paradigm to perform pattern recognition by storing information about the complement set (non self) of the patterns to be recognized (self)
Wednesday, November 28, 2007
Friday, November 09, 2007
Forecasting Powerdemand Using Artificial Neural Networks
Electricity load forecasting is very important for the reliable operation of electricitycompanies. Load forecasting helps to make important decisions for generatingelectric power. Load forecasts are extremely important for energy suppliers, financialinstitutions, and other participants in electric energy generation, transmission,distribution, and markets.
Load forecasts can be divided into three categories:
1. Short-term forecasts - usually from one hour to one week
2. Medium forecasts - usually from a week to a year and
3. Long-term forecasts - longer than a year
The forecasts for different time horizons are important for different operations. Short-term load forecasting can help to estimate load flows and to make decisions
that can prevent overloading. Such decisions can be used to the improvement of network reliability and to the reduced occurrences of equipment failures. Long term forecasting helps to know the electric load that want in the future and take necessary actions like building more power stations, for fulfill the needs of people in the country. Artificial Neural networks have been known of for a long time. Today, neural networks are broadly used in science and commercial products. It can be forecasted a daily peak load by using actual load of some similar days of target day. But in that case, there are limitations to improve forecasting accuracy by these methods. Neural networks are essentially non-linear circuits that have the demonstrated capability to do non-linear curve fitting. So ANNs can be applied for the forecasting problems.
Load forecasts can be divided into three categories:
1. Short-term forecasts - usually from one hour to one week
2. Medium forecasts - usually from a week to a year and
3. Long-term forecasts - longer than a year
The forecasts for different time horizons are important for different operations. Short-term load forecasting can help to estimate load flows and to make decisions
that can prevent overloading. Such decisions can be used to the improvement of network reliability and to the reduced occurrences of equipment failures. Long term forecasting helps to know the electric load that want in the future and take necessary actions like building more power stations, for fulfill the needs of people in the country. Artificial Neural networks have been known of for a long time. Today, neural networks are broadly used in science and commercial products. It can be forecasted a daily peak load by using actual load of some similar days of target day. But in that case, there are limitations to improve forecasting accuracy by these methods. Neural networks are essentially non-linear circuits that have the demonstrated capability to do non-linear curve fitting. So ANNs can be applied for the forecasting problems.
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