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Evolutionary Learning


Explicit Components

Loop

Octopus


Supervised Learning

Utilities

View

Network Training Settings (oSL)

Settings for RPROP Supervised Learning of an ANN

Inputs

Name	ID	Description	Type
MaxSteps	MaxSteps	Maximum Steps for Learning; when reached, Learning ends. 0 for unlimited steps.	Integer
MaxTime	MaxTime	Time Limit in milliseconds; 0 for no limit; when reached, Learning ends.	Integer
MinErr	MinErr	Minimum Error between Input and Output for Learning; when reached, Learning ends.	Number
Memory Limit	MemL	Percent of main memory to remain free; if dropping below, learning is stopped.	Number
Divergence Steps	DivS	Number of consecutive steps with worsening error after which learning is stopped. 0 to disable this check.	Integer
Hist	Hist	Record History? Set to true to get an ANN for each Step of the Learning	Boolean
Type	Type	Type of Resilient Propagation Method [0-RPROPp \| 1-RPROPm \| 2-iRPROPp \| 3-iRPROPm]	Integer
InitUpd	InitUpd	Initial update Parameter for Resilient Propagation Algorithm	Number
MaxStep	MaxStep	MaxStep Parameter for Resilient Propagation Algorithm	Integer
Cores	Cores	Number of processor cores to use; 0 to figure out the maximal optimum automatically.	Integer
Reset	Reset	Switch to false to store the training model between updates of the component and continue training on it. The default resets the model on every update. If a SeedANN is supplied, training always starts from that one - then this switch does not affect anything.	Boolean

Outputs

Name	ID	Description	Type
S	S	Settings for Supervised Example Learning of Networks using Resilient Propagation from the Encog library	RPROP Learning Settings