Evolutionary Polymorphic Neural Networks in Chemical Engineering Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Li Gao",
  title =        "Evolutionary Polymorphic Neural Networks in Chemical
                 Engineering Modeling",
  school =       "Department of Chemical Engineering, New Jersey
                 Institute of Technology",
  year =         "2001",
  address =      "USA",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Polymorphic Neural Network (EPNN), Artificial
                 intelligence, Evolutionary computing",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/LiGao_thesis.pdf",
  size =         "146 pages",
  abstract =     "Evolutionary Polymorphic Neural Network (EPNN) is a
                 novel approach to modeling chemical, biochemical and
                 physical processes. This approach has its basis in
                 modern artificial intelligence, especially neural
                 networks and evolutionary computing. EPNN can perform
                 networked symbolic regressions for input-output data,
                 while providing information about both the structure
                 and complexity of a process during its own

                 In this work three different processes are modeled: 1.
                 A dynamic neutralisation process. 2. An aqueous
                 two-phase system. 3. Reduction of a biodegradation
                 model. In all three cases, EPNN shows better or at
                 least equal performances over published data than
                 traditional thermodynamics /transport or neural network
                 models. Furthermore, in those cases where traditional
                 modeling parameters are difficult to determine, EPNN
                 can be used as an auxiliary tool to produce equivalent
                 empirical formulae for the target process. Feedback
                 links in EPNN network can be formed through training
                 (evolution) to perform multiple steps ahead predictions
                 for dynamic nonlinear systems. Unlike existing
                 applications combining neural networks and genetic
                 algorithms, symbolic formulae can be extracted from
                 EPNN modeling results for further theoretical analysis
                 and process optimisation.

                 EPNN system can also be used for data prediction
                 tuning. In which case, only a minimum number of initial
                 system conditions need to be adjusted. Therefore, the
                 network structure of EPNN is more flexible and
                 adaptable than traditional neural networks.

                 Due to the polymorphic and evolutionary nature of the
                 EPNN system, the initially randomised values of
                 constants in EPNN networks will converge to the same or
                 similar forms of functions in separate runs until the
                 training process ends. The EPNN system is not sensitive
                 to differences in initial values of the EPNN
                 population. However, if there exists significant larger
                 noise in one or more data sets in the whole data
                 composition, the EPNN system will probably fail to
                 converge to a satisfactory level of prediction on these
                 data sets.

                 EPNN networks with a relatively small number of neurons
                 can achieve similar or better performance than both
                 traditional thermodynamic and neural network

                 The developed EPNN approach provides alternative
                 methods for efficiently modeling complex, dynamic or
                 steady-state chemical processes. EPNN is capable of
                 producing symbolic empirical formulae for chemical
                 processes, regardless of whether or not traditional
                 thermodynamic models are available or can be applied.
                 The EPNN approach does overcome some of the limitations
                 of traditional thermodynamic /transport models and
                 traditional neural network models.",
  notes =        "Advisory Committee: Loney, Norman W. Baltzis, Basil C.
                 Barat, Robert B. Knox, Dana E. Blackmore, Denis Wasser,
                 Daniel J.",

Genetic Programming entries for Li Gao