Knowledge Fusion

Fuzzy logic

Fuzzy logic is a powerful problem-solving methodology with a myriad of applications in embedded control and information processing . fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. Fuzzy logic incorporates an alternative way of thinking, which allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience. Fuzzy Logic has been found to be very suitable for embedded control applications, in the automotive industry, in aerospace, in consumer electronics & in manufacturing.
The main idea behind fuzzy logic is that there are many cases here TRUE and FALSE or ON and OFF fail to describe a given situation. These cases require a sliding scale where variables can be measured as PARTLY ON or MOSTLY TRUE and PARTLY FALSE. With fuzzy logic, an object can be a member of multiple sets with a different degree of membership in each set. A degree of membership in a set is based on a scale from 0 to 1 with 1 being complete membership and 0 being no membership. Each rule takes number of inputs and determines its appropriate output. All of the outputs from the individual rules are combined into one term called the Logical Sum.

Neural Networks are a different paradigm for computing:

• von Neumann machines are based on the processing/memory abstraction of human information processing.
• neural networks are based on the parallel architecture of animal brains.

Neural networks are a form of multiprocessor computer system, with

• simple processing elements
• a high degree of interconnection
• simple scalar messages
• adaptive interaction between elements

A biological neuron may have as many as 10,000 different inputs, and may send its output (the presence or absence of a short-duration spike) to many other neurons. Neurons are wired up in a 3-dimensional pattern.

Real brains, however, are orders of magnitude more complex than any artificial neural network so far considered.

Example: A simple single unit adaptive network:

neural networks

The network has 2 inputs, and one output. All are binary. The output is

1 if W₀ *I₀ + W₁ * I₁ + W_b > 0

0 if W₀ *I₀ + W₁ * I₁ + W_b <= 0

We want it to learn simple OR: output 1 if either I₀ or I₁ is 1.

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