POPFNN: A Pseudo Outer-product Based Fuzzy Neural Network

Zhou, Ruqi; Quek, Chai

doi:10.1016/s0893-6080(96)00027-5

Cited by 117 publications

(38 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The functions performed by each layer in POPFNN correspond strictly to the inference steps in the truth value restriction method in fuzzy logic (Mantaras, 1990). This correspondence gives it a strong theoretical basis and is reported in Zhou and Quek (1996).…”

Section: Pseudo Outer Product Based Fuzzy Neural Networksupporting

confidence: 66%

See 1 more Smart Citation

A novel neuro-cognitive approach to modeling traffic control and flow based on fuzzy neural techniques

Chong

Quek

Loh

2009

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

Section: Pseudo Outer Product Based Fuzzy Neural Networksupporting

confidence: 66%

“…A pseudo outer-product based fuzzy neural network (Zhou & Quek, 1996), POPFNN (Fig. 3), is an integrated fuzzy neural network, which accomplishes the whole process from fuzzification, fuzzy inference to the defuzzification process.…”

Section: Pseudo Outer Product Based Fuzzy Neural Networkmentioning

confidence: 99%

A novel neuro-cognitive approach to modeling traffic control and flow based on fuzzy neural techniques

Chong

Quek

Loh

2009

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

“…Pseudo Outer-Product based Fuzzy Neural Networks (POPFNN) is a family of neuro-fuzzy systems that is based on the linguistic fuzzy model [16]. Three members of POPFNN exists in the literature, namely: POPFNN-CRI(S) [25] which is based on commonly accepted fuzzy Compositional Rule of Inference, POPFNN-TVR [26] which is based on Truth Value Restriction, and POPFNN-AARS(S) [27] which is based on the Approximate Analogical Reasoning Scheme. The POPFNN architecture is a five-layer neural network as shown in Fig.…”

Section: Rspop Fuzzy Neural Networkmentioning

confidence: 99%

“…Various fuzzy membership learning can be used in POPFNN: Learning Vector Quantization (LVQ) [28], Fuzzy Kohonen Partitioning (FKP) [25] or Discrete Incremental Clustering (DIC) [29]. The Pseudo Outer Product (POP) algorithm [26] or its variant LazyPOP [16] is used in the POPFNN family of networks to identify fuzzy rules.…”

Section: Rspop Fuzzy Neural Networkmentioning

confidence: 99%

“…Although POP [26] is a simple, easy to understand onepass learning algorithm that performs fast, reliable and efficient consequence identification for fuzzy rules, it has to compute all possible rules in its learning process. As a result, the fuzzy rules derived using POP usually consists of either all possible rules or a large number of rules.…”

Section: Rspop Fuzzy Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

2007

Self Cite

View full text Add to dashboard Cite

The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications. Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System, that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system; demonstrating the system's ability to filter out erroneous trading signals generated by technical rules and to preempt any losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter's price, without a need for predicting target prices.

show abstract

Investment portfolio balancing: application of a generic self‐organizing fuzzy neural network (GenSoFNN)

Quek

Yow

Cheng

et al. 2009

Intelligent Systems in Accounting, Finance and Management

View full text Add to dashboard Cite

In contrast to short‐term stock trading, portfolio managers are interested in the medium‐ to long‐term peaks and troughs of the stock price cycles as signals to balance their stock portfolios – the predicted trough is the signal to buy the stock and the predicted peak is the signal to sell the stock. As statistical models are generally inadequate or incapable of providing such portfolio balancing signals, we propose using the generic self‐organizing fuzzy neural network (GenSoFNN)—a fuzzy neural system – as a tool for portfolio balancing. The network adopts the supervised learning approach to detect inflection points in the stock price cycles, and a modified locally weighted regression algorithm is employed to smooth the stock cycles. The GenSoFNN‐based portfolio balancing system was evaluated with experiments conducted using 23 stocks from the New York Stock Exchange and NASDAQ, and the results showed an average profit return of 65.66%. The contributions of the proposed GenSoFNN intelligent portfolio balancing system are twofold: it can be used as an efficient trading solution and it can provide decision support in trading via its generated rules. Copyright © 2009 John Wiley & Sons, Ltd.

show abstract

POPFNN: A Pseudo Outer-product Based Fuzzy Neural Network

Cited by 117 publications

References 17 publications

A novel neuro-cognitive approach to modeling traffic control and flow based on fuzzy neural techniques

A novel neuro-cognitive approach to modeling traffic control and flow based on fuzzy neural techniques

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

Investment portfolio balancing: application of a generic self‐organizing fuzzy neural network (GenSoFNN)

Contact Info

Product

Resources

About