MCMAC–CVT: a novel on-line associative memory based CVT transmission control system

Ang, Kai Keng; Quek, Chai; Wahab, Abdul

doi:10.1016/s0893-6080(01)00143-5

Cited by 30 publications

(13 citation statements)

References 6 publications

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“…Advanced knowledge reduction techniques are also developed to improve the interpretability of the semantic memory structures (Ang & Quek, 2005;Liu, Quek, & Ng, 2007;Quah & Quek, 2007). These have been applied to biometric (Quek, Tan, & Sagar, 2001;Quek & Zhou, 2002;Zhou, Quek, & Ng, 1995), banking (Ang & Quek, 2006;Tung, Quek, & Cheng, 2004), transportation (Ang, Quek, & Wahab, 2002;Pasquier, Quek, & Toh, 2001;Pasquier, Quek, Tan, & Chee, 2001) as well medical decision support (Quek, Pasquier, & Lim, 2006;Tung & Quek, 2005;Tan, Quek, & Ng, 2005). …”

Section: Resultsmentioning

confidence: 99%

A nature inspired Ying–Yang approach for intelligent decision support in bank solvency analysis

Nguyen

Shi

Quek

2008

Expert Systems with Applications

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Section: Resultsmentioning

confidence: 99%

A nature inspired Ying–Yang approach for intelligent decision support in bank solvency analysis

Nguyen

Shi

Quek

2008

Expert Systems with Applications

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“…The C2i lab undertakes intense research in the study and development of advanced brain-inspired learning memory architectures [74]- [78] for the modeling of complex, dynamic, and nonlinear systems. These techniques have been successfully applied to numerous novel applications such as automated driving [58], signature forgery detection [79], gear control for the continuous variable transmission (CVT) system in an automobile [80], fingerprint verification [81], bank failure classification and early warning system (EWS) [82], computational finance [83], [84], as well as in the biomedical engineering domain [85], [86]. …”

Section: Discussionmentioning

confidence: 99%

Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

Teddy

Lai²,

Quek

2007

IEEE Trans. Neural Netw.

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Abstract-The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: 1) a constant output resolution associated with the entire input space and 2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages.Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC), learning convergence, nonuniform quantization.

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“…These research endeavors culminated with the developments of the human hippocampus-inspired learning memory systems such as GenSoFNN [10,28,35], Pseudo Adaptive Complementary Learning networks [42,43] and POPFNN [44][45][46], as well as cerebellar-based computational models [47,48] for the modeling of complex, dynamic and non-linear problem domains. The application of these brain-inspired learning memory systems is actively pursued, and they have been successfully applied to automated driving [49], signature forgery detection [50], gear control for continuous-variable-transmission in automobile [51], fingerprint verification [52], medical decision-support [28,53] and computational finance [35,46,54].…”

Section: Discussionmentioning

confidence: 99%

A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty

Tung

Quek

2007

Neural Comput & Applic

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Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert's knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a braininspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging.

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MCMAC–CVT: a novel on-line associative memory based CVT transmission control system

Cited by 30 publications

References 6 publications

A nature inspired Ying–Yang approach for intelligent decision support in bank solvency analysis

A nature inspired Ying–Yang approach for intelligent decision support in bank solvency analysis

Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty

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