Other Model-Based Synthesis Systems

The INDUCE systems (see [Michalski 84]) were among the first to address the issues of using constructive rules of inductive inference. The learning mechanism is a bottom-up approximation-driven one, though heavily based on heuristics. 

The LINUS system of [Lavrab and Dzeroski 94] learns relational concept descriptions, but works by first transforming the examples into propositional form, and then using propositional attribute-value learners (which may handle noisy examples). 

A completely different approach is advocated by [Hagiya 90]. He re-formulates Summers recurrence relation detection mechanism in a logic framework, using higher-order unification in a type theory with a recursion operator. The method is extended to synthesizing deductive proofs-by-induction from concrete sample proofs. 

The work of [Jorge and Brazdil 94] is another attempt at trace-based synthesis of logic programs. Their specifications by examples are augmented with partial execution traces, called algorithm sketches. 

---

We have no empirical data (synthesis times, example consumption,. ..) yet about a comparison between SYNAPSE and its related systems, such as MIS [Shapiro 82], CLINT [De Raedt and Bruynooghe 92b], FOIL [Quinlan 90], or the other model-based systems of the ELP community (see Section 3.4), or the trace-based systems of the LISP community (see Section 3.3). But we may make some predictions by comparing the underlying synthesis mechanisms instead. This requires some analysis first. 

Many of the aforementioned heuristic decentralized control synthesis approaches rely on engineering judgement rather than rigorous analysis. On the other hand, the implementation of advanced, model-based, control strategies for process systems is hindered by the often overwhelming size and complexity of their dynamic models. The results cited above indicate that the design of fully centralized controllers on the basis of entire process models is impractical, such... 

Fig. 6.11. Aperiodic oscillations (chaos) in cAMP synthesis predicted by the model based on receptor desensitization. The chaotic behaviour is obtained by numerical integration of the seven-variable system (6.2), for v = 7.545 x 10 s, other parameter values are those of fig. 6.1, divided by 10 for constants expressed in s (Martiel Goldbeter, 1985a).

This chapter begins with an overview of systems theory then a model that depicts the textiles recycling processes, particularly as it pertains to apparel. After that, a micro-macro model using social systems theory is presented. Finally, a synthesis of how systems theory provides a useful tool to project future trends for the textile and apparel recycling process is presented. It is important to note that this work is based primarily on the processes as they are in the United States. The research is based on over five years of qualitative data collection on, primarily, apparel and other fashion products consumed throughout the USA and the world. 

Other approaches model hardware systems by net-based representations, such as the Petri-net model and its derivatives [Pet83], and event based models [Bor88a]. Synthesis can be seen as a series of transformations on the net model so that the resulting net model satisfies certain properties necessary for hardware synthesis. Restrictions of Petri-nets have already been widely used for asynchronous circuit modeling and synthesis [Chu87, MMB89]. There is active... 

Despite the broad definition of chemometrics, the most important part of it is the application of multivariate data analysis to chemistry-relevant data. Chemistry deals with compounds, their properties, and their transformations into other compounds. Major tasks of chemists are the analysis of complex mixtures, the synthesis of compounds with desired properties, and the construction and operation of chemical technological plants. However, chemical/physical systems of practical interest are often very complicated and cannot be described sufficiently by theory. Actually, a typical chemometrics approach is not based on first principles—that means scientific laws and mles of nature—but is data driven. Multivariate statistical data analysis is a powerful tool for analyzing and structuring data sets that have been obtained from such systems, and for making empirical mathematical models that are for instance capable to predict the values of important properties not directly measurable (Figure 1.1). 

The authors also investigated the mode of activation of these BINOL-derived catalysts. They proposed an oligomeric structure, in which one Ln-BINOL moiety acts as a Brpnsted base, that deprotonates the hydroperoxide and the other moiety acts as Lewis acid, which activates the enone and controls its orientation towards the oxidant . This model explains the observed chiral amplification effect, that is the ee of the epoxide product exceeds the ee of the catalyst. The stereoselective synthesis of cw-epoxyketones from acyclic cw-enones is difficult due to the tendency of the cw-enones to isomerize to the more stable fraw5-derivatives during the oxidation. In 1998, Shibasaki and coworkers reported that the ytterbium-(f )-3-hydroxymethyl-BINOL system also showed catalytic activity for the oxidation of aliphatic (Z)-enones 129 to cw-epoxides 130 with good yields... 

---