Retrofit Self-Optimizing Control: A Step Forward Toward Real Implementation

Toward optimizing a self-creating neural network

This paper optimizes the performance of the growing cell structures (GCS) model in learning topology and vector quantization. Each node in GCS is attached with a resource counter. During the competitive learning process, the counter of the best-matching node is increased by a defined resource measure after each input presentation, and then all resource counters are decayed by a factor alpha. We...

One step forward

Self-optimizing control - A survey

Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection...

Retrofit Control: Localization of Controller Design and Implementation

In this paper, we propose a retrofit control method for stable network systems. The proposed approach is a decentralized control method that, rather than an entire system model, requires a model of the subsystem of interest for controller design. To design the retrofit controller, we use a novel approach based on hierarchical state-space expansion that generates a higherdimensional cascade real...

Toward a Real-Time Waveguide Mesh Implementation

This work presents an analytic solution to a 2-D waveguide mesh, made square with input and output at the center to produce a symmetry that reduces redundancy and increases computational savings. The result is a frequency-domain representation of the mesh, a parametric transfer function describing the ratio of output to input, symbolically computed to retain parameters. In this form, the mesh, ...