Change propagation analysis in complex technical systems

Understanding how and why changes propagate during engineering design is critical because most products and systems emerge from predecessors and not through clean sheet design. This paper applies change propagation analysis methods and extends prior reasoning through examination of a large data set from industry including 41,500 change requests, spanning 8 years during the design of a complex sensor system. Different methods are used to analyze the data and the results are compared to each other and evaluated in the context of previous findings. In particular the networks of connected parent, child and sibling changes are resolved over time and mapped to 46 subsystem areas. A normalized change propagation index (CPI) is then developed, showing the relative strength of each area on the absorber-multiplier spectrum between -1 and +1. Multipliers send out more changes than they receive and are good candidates for more focused change management. Another interesting finding is the quantitative confirmation of the "ripple" change pattern. Unlike the earlier prediction, however, it was found that the peak of cyclical change activity occurred late in the program driven by systems integration and functional testing. Patterns emerged from the data and offer clear implications for technical change management approaches in system design. Copyright © 2007 by ASME.

Dynamical structure function identifiability conditions enabling signal structure reconstruction

Networks of controlled dynamical systems exhibit a variety of interconnection patterns that could be interpreted as the structure of the system. One such interpretation of system structure is a system's signal structure, characterized as the open-loop causal dependencies among manifest variables and represented by its dynamical structure function. Although this notion of structure is among the weakest available, previous work has shown that if no a priori structural information is known about the system, not even the Boolean structure of the dynamical structure function is identifiable. Consequently, one method previously suggested for obtaining the necessary a priori structural information is to leverage knowledge about target specificity of the controlled inputs. This work extends these results to demonstrate precisely the a priori structural information that is both necessary and sufficient to reconstruct the network from input-output data. This extension is important because it significantly broadens the applicability of the identifiability conditions, enabling the design of network reconstruction experiments that were previously impossible due to practical constraints on the types of actuation mechanisms available to the engineer or scientist. The work is motivated by the proteomics problem of reconstructing the Per-Arnt-Sim Kinase pathway used in the metabolism of sugars. © 2012 IEEE.

Code-specific policy gradient rules for spiking neurons

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect. We use the framework of Williams (1992) to derive learning rules for arbitrary neural codes. For illustration, we present policy-gradient rules for three different example codes - a spike count code, a spike timing code and the most general "full spike train" code - and test them on simple model problems. In addition to classical synaptic learning, we derive learning rules for intrinsic parameters that control the excitability of the neuron. The spike count learning rule has structural similarities with established Bienenstock-Cooper-Munro rules. If the distribution of the relevant spike train features belongs to the natural exponential family, the learning rules have a characteristic shape that raises interesting prediction problems.