A cerebellar associative memory approach to option pricing and arbitrage trading

Teddy, S. D.; Lai, Edmund Ming-Kit; Quek, Chai

doi:10.1016/j.neucom.2008.04.039

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…From Fig. 5 and (8), (27), and (28), one can deduce that and . Hence, there is no tuning of the fuzzy labels and .…”

Section: B a Localized Approach To Parameter Adaptationmentioning

confidence: 88%

“…Notice that (37) and (38) are similar to the learning equations of the output fuzzy set parameters [see (27) and (28) spread of increases (subjected to maximum ) when or decreases (subjected to minimum ) if . This concludes a brief discussion on the learning mechanisms of the LPA procedure proposed for the tuning of the fuzzy set parameters in the eFSM model.…”

Section: B a Localized Approach To Parameter Adaptationmentioning

confidence: 96%

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eFSM—A Novel Online Neural-Fuzzy Semantic Memory Model

Tung

Quek

2010

IEEE Trans. Neural Netw.

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Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. This enables eFSM to maintain a current and compact set of Mamdani-type if-then fuzzy rules that collectively generalizes and describes the salient associative mappings between the inputs and outputs of the underlying process being modeled. The learning and modeling performances of the proposed eFSM are evaluated using several benchmark applications and the results are encouraging.

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“…From Fig. 5 and (8), (27), and (28), one can deduce that and . Hence, there is no tuning of the fuzzy labels and .…”

Section: B a Localized Approach To Parameter Adaptationmentioning

confidence: 88%

Section: B a Localized Approach To Parameter Adaptationmentioning

confidence: 96%

eFSM—A Novel Online Neural-Fuzzy Semantic Memory Model

Tung

Quek

2010

IEEE Trans. Neural Netw.

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“…Although many neural fuzzy based trading models have been developed for stock trading or currency trading, only a few current works [123][124] are focused on enhancing and protecting trading results using options. Options, as a derivative security, provide a means to manage financial risks.…”

Section: Discussionmentioning

confidence: 99%

“…In [123], Tung and Quek proposed a self-organizing network, GenSoFNN, which emulates the information handling and knowledge acquisition of the hippocampal memory [125]. In [124],…”

Section: Discussionmentioning

confidence: 99%

Self evolving Takagi-Sugeno-Kang fuzzy neural network.

Nam¹

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“…Tibshirani 1990], Cerebellar Associative Memory Approach (CMAC) [Nguyen, Shi et al 2006;Teddy, Lai et al 2008;Teddy, Quek et al 2008], Neural Networks (NN) [Bishop 1995], Neural Fuzzy System (NFS) [Harris, Hong et al 2002;Nguyen and Shi 2008;Singh, Quek et al 2008], Radial Basis Functions (RBF) [Powell 1987] and Relevance Vector Machine (RVM) [Tipping 2001] belong to this category. On the other hand, there are no explicit parameters in non-parametric models.…”

Section: Most Of the Traditional Models Such As Generalized Additive mentioning

confidence: 99%

Computationally efficient models for high-dimensional and large-scale classification problems

Ma¹

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Generally there are two main objectives in designing modern learning models when handling the problems with high-dimensional input spaces and a large amount of data. Firstly the model's effectiveness in terms of a good accuracy needs to be met and secondly the model's efficiency in terms of scalability and computation complexity needs to suffice. In practice these objectives require different types of learning models to solve different difficulties. In the case of the parametric models such as the radial basis function (RBF), the main difficulty is in the deterioration in accuracy and increase in computation complexity for high-dimensional data, which can be caused by the inductive nature of learning problems and the curse of dimensionality. While in the case of nonparametric models such as the Gaussian process (GP), the computing demand could become extremely high when there is a large amount of data to be processed. These difficulties pose the main obstacles preventing many successful traditional models from being applied to high-dimensional and large-scale data applications. Several extensions have been proposed in recent years based on the traditional models, for both parametric and nonparametric types. Some of these extensions include the extended radial basis function (ENRBF) and the sparse pseudo-input Gaussian process (SPGP) models. With proper modifications, these models are both effective in improving the learning performance and efficient in reducing the computation complexity. However all of these models are meant to solve regression problems thus further extensions of these regression models are required to ensure that it can be applied to classification problems. Unfortunately such extensions are neither easy nor straightforward and careless extensions can bring about unpleasant results such as masking problems due to

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A cerebellar associative memory approach to option pricing and arbitrage trading

Cited by 9 publications

References 28 publications

eFSM—A Novel Online Neural-Fuzzy Semantic Memory Model

eFSM—A Novel Online Neural-Fuzzy Semantic Memory Model

Self evolving Takagi-Sugeno-Kang fuzzy neural network.

Computationally efficient models for high-dimensional and large-scale classification problems

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