Structure of a trainable state machine

The present application relates generally to trainable state machines such as robotic controllers, and more particularly to the logical structure of the interrelated variables and functions of trainable state machines. The algorithm used for training a state machine has been disclosed in another application. The objective of this application is to reduce this theoretical framework to a more concrete structure. The structure disclosed involves the use of nodes to perform the function of state equations and calculating the value of particular state variables. After the nodes where classified as either Lead-Type Nodes or Non-Lead-Type Nodes, this classification was used to define a structure of nodes to build a State Machine Block. This State Machine Block model also restricts the location of system inputs and system outputs.The internal structure of a node is discussed with the components being a Function Block and a Complex Impedance Network. The Function Block is typically a multivariable power series and the Complex Impedance Network is a linear circuit of resistors and capacitors. Such a Complex Impedance Network is referred to as an Electrical Component Model. A Complex Impedance Network can also be modeled as a z-transform circuit referred to as the Z-Transform Model. To assist in processing the signal level and the derivative variable through this structure, some C++ code structure are developed and discussed.