Artificial Neural Networks

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Artificial Neural Networks by Mind Map: Artificial Neural Networks

1. Process

1.1. 1. Input data into a neural net & ANN guesses the output.

1.2. 2. Compare prediction with actual (actual) value.

1.3. 3. If it's incorrect guess, Ann examines itself to determine which parameter to adjust.

1.4. 4. Repeat the process.

2. Ability of modeling complex problems, via program based on Trial & Error.

3. Functions

3.1. State Function

3.1.1. Consolidates the weights of the various inputs a single value.

3.2. Transfer Function

3.2.1. Processes this state value and makes the output.

4. Advantages

4.1. No need for programming.

4.2. Reduce number of experts need.

4.3. Adaptable to changed inputs.

4.4. No need for Knowledge base.

4.5. Dynamic and improve with use.

4.6. Ability to process errors & incomplete data.

4.7. Generalization from specific information.

4.8. Common sense into the problem-solving domain.

5. Disadvantages

5.1. Cannot explain their inference.

5.2. "Black Box" makes accountability & reliability issues difficult.

5.3. Repeating the training process in time consuming.

5.4. Require "faith" gave to the output.

6. Learning Paradigms

6.1. Supervised

6.1.1. ANN receives input, but not feedback about desired results. Develops clusters of training records (based on data similarities).

6.2. Unsupervised

6.2.1. ANN compare it's guess to feedback containing the desired results. Back propagation (most common), does the comparison with squared errors

7. ANN Basic Layers

7.1. 1. Input

7.1.1. Receive the data

7.2. 2. Internal \ Hidden

7.2.1. Process the data

7.3. 3. Output

7.3.1. Pass on the final result to the net