
TL;DR
This paper introduces a novel attractor FCM model that combines gradient descent, physics constraints, and recursive updates to improve convergence and learning efficiency.
Contribution
It presents a new attractor FCM architecture with residual memory, a fixed point anchor, and an adaptive Newton-based learning algorithm for enhanced convergence.
Findings
The model converges to fixed points effectively.
The adaptive Newton method improves error minimization.
Residual memory preserves system state during learning.
Abstract
In this paper an attractor FCM is created, tested, and analyzed. This FCM is neither a hebbian based nor agentic, nor a hybrid; it rather is a gradient descent based, physics constrained, Jacobian version of an FCM. Moreover, this model has several quirks; it uses residual memory, back propagation through time, and a fixed point anchor that is recursively implemented to update its weights. The residuals update the recursive part without losing the system memory. The model's anchor enables it to converge in a fixed point for which back propagation through time unrolls it and ensures that the error minimization is for an accurate gradient. Furthermore, a new learning algorithm is utilized. The Newton's method finds the system's fixed point attractor and then gradient descend is adaptively changing the landscape; an adaptive term is used to directly manipulate the weights through the…
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