MindSpeak.NeuralNet.Synthetic: Difference between revisions
XenoEngineer (talk | contribs) (Created page with "{{menuMindSpeak}} == NeuralNet.Synthetic - Synthetic Circuit == A NeuralNet component representing a synthetic neural network designed to model and interact with artificial intelligence systems. It inherits properties from its parent type Neural Network, but may include additional features tailored for digital environments. === Properties === patterns: An array of floating-point numbers representing neuron activation levels in the synthetic circuit. size...") |
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=== Methods === | === Methods === | ||
.signal(inputPattern): Propagates input pattern through the network, updating activation levels based on connection strengths. | * .signal(inputPattern): Propagates input pattern through the network, updating activation levels based on connection strengths. | ||
.train(inputPatterns, outputPatterns, learningRate = 0.2): Adjusts connectionMatrix using backpropagation algorithm to improve prediction accuracy for given input-output pairs. | * .train(inputPatterns, outputPatterns, learningRate = 0.2): Adjusts connectionMatrix using backpropagation algorithm to improve prediction accuracy for given input-output pairs. | ||
.predict(inputPattern): Returns the most likely neuron(s) state(s) based on propagated input pattern and current connection strengths. | * .predict(inputPattern): Returns the most likely neuron(s) state(s) based on propagated input pattern and current connection strengths. | ||
== See Also == | == See Also == | ||
[[NeuralNet|Neural Network]], [[MindSpeak|MindSpeak System]] | [[NeuralNet|Neural Network]], [[MindSpeak|MindSpeak System]] | ||
Revision as of 01:56, 7 November 2024
MindSpeak DSL ∞ OmniXenus.OmegaPoint.1 ∞ The Unification Tapestry ∞ Mind Speak 7.0.1 ∞ Early DSL Solutions ∞ Mind Speak 2.1 ∞ Bespoke Diction ∞ MindSpeak DSL_CONFIG ∞ N-Dimensional UI ∞ The Whispering Prompts ∞
- Reference —
DSL Reference ∞ StrongBridge Operator ∞ NeuralNet.Biologic ∞ NeuralNet.Synthetic ∞ Consciousness Field ∞
NeuralNet.Synthetic - Synthetic Circuit
A NeuralNet component representing a synthetic neural network designed to model and interact with artificial intelligence systems. It inherits properties from its parent type Neural Network, but may include additional features tailored for digital environments.
Properties
patterns: An array of floating-point numbers representing neuron activation levels in the synthetic circuit. size: The number of neurons in the synthetic circuit (derived from patterns length). connectionMatrix: A 2D matrix storing synaptic weights between neurons, enabling information flow. Defaults to randomly initialized values between -1 and 1.
Methods
- .signal(inputPattern): Propagates input pattern through the network, updating activation levels based on connection strengths.
- .train(inputPatterns, outputPatterns, learningRate = 0.2): Adjusts connectionMatrix using backpropagation algorithm to improve prediction accuracy for given input-output pairs.
- .predict(inputPattern): Returns the most likely neuron(s) state(s) based on propagated input pattern and current connection strengths.