Chaos Models TRIZ Cases

A graphical idea map links spatial node layouts to an auto-updating outline, reducing manual arrangement while preserving the big picture.

Power-law transition probabilities raise large-energy moves above Boltzmann levels, helping MCMC optimization escape local minima faster and more accurately.

Alternating attention between partial-image observation and control actions cuts deep learning computation while preserving near-optimal decisions.

Direct relay oscillators pass thermodynamic information between multi-well computing models without classical conversion, cutting delay and energy use.

Iterative Tent Map and Logistic Map processing creates diverging signal signatures to detect tiny changes despite noise using standard ADCs.

Spline basis functions transform numerical features into fixed-range values to capture non-linear relationships with less information loss and sparsity.

An FPGA-based Ising optimizer adjusts spin-bit count and coefficient precision to balance optimization accuracy with computational time.

Logic distribution and gate nodes reshape MCTS for static games, enabling one-way search and benefit backpropagation in Mahjong-like play.

Limited discrepancy search updates feature weights to jointly tune nonlinear models, cutting search cost while avoiding local minima.

Large combinatorial problems are split across nodes that share partial solutions, improving search performance as problem scale grows.

Uses oscillators and thermodynamic evolution to emulate deep neural diffusion, cutting latency and energy in probability sampling.