The potential of fuzzy neural networks in the realization of approximate reasoning engines

Gobi, A.F.; Pedrycz, Witold

doi:10.1016/j.fss.2006.06.006

Cited by 16 publications

(5 citation statements)

References 42 publications

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“…Otherwise, the digital learning accelerator, consisting of a host RISC processor, a 4-kB parameter cache and a learning accelerator, is responsible for updating new training parameters of VANFIS. Since learning algorithm is based on complex arithmetic calculation, high resolution and precise calculation are required for updating new parameters [20]. For the communication between two domains, ADC/DAC arrays are implemented while consuming extra costs of power, latency, and area.…”

Section: Analog/digital Mixed-mode System Implementationmentioning

confidence: 99%

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1.2-mW Online Learning Mixed-Mode Intelligent Inference Engine for Low-Power Real-Time Object Recognition Processor

Lee

Yoo

2013

IEEE Trans. VLSI Syst.

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Object recognition is computationally intensive and it is challenging to meet 30-f/s real-time processing demands under sub-watt low-power constraints of mobile platforms even for heterogeneous many-core architecture. In this paper, an intelligent inference engine (IIE) is proposed as a hardware controller for a many-core processor to satisfy the requirements of low-power real-time object recognition. The IIE exploits learning and inference capabilities of the neurofuzzy system by adopting the versatile adaptive neurofuzzy inference system (VANFIS) with the proposed hardware-oriented learning algorithm. Using the programmable VANFIS, the IIE can configure its hardware topology adaptively for different target classifications. Its architecture contains analog/digital mixed-mode neurofuzzy circuits for updating online parameters to increase attention efficiency of object recognition process. It is implemented in 0.13-µm CMOS process and achieves 1.2-mW power consumption with 94% average classification accuracy within 1-µs operation delay. The 0.765-mm 2 IIE achieves 76% attention efficiency and reduces power and processing delay of the 50-mm 2 image processor by up to 37% and 28%, respectively, when 96% recognition accuracy is achieved.

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Section: Analog/digital Mixed-mode System Implementationmentioning

confidence: 99%

“…For analog inference and digital learning operations, it is known that a digital processor requires more than 8-bit resolution and an analog processor requires lower resolution for accurate operation [20]. Since resolution of DAC/ADC affects power consumption and area overhead exponentially, as shown in Fig.…”

Section: Dac/adc Optimizationmentioning

confidence: 99%

1.2-mW Online Learning Mixed-Mode Intelligent Inference Engine for Low-Power Real-Time Object Recognition Processor

Lee

Yoo

2013

IEEE Trans. VLSI Syst.

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“…Recent striking approaches have concentrated on fuzzy clustering method (FCM) whose applications range from data analysis, pattern recognition, image segmentation, group-positioning analysis, satellite images, financial analysis,.. With the growing demands for the exploitation of intelligent and highly autonomous systems, it would be beneficial to combine robust learning capabilities with a high level of knowledge interpretability. Fuzzy neuro computation supports a new paradigm of intelligent information processing [1,2,[5][6][7][8], in which we are able to achieve this powerful combination. Nowadays, W.Pedrycz et al presented some knowledge-based clustering methods, including context fuzzy C-means method (CFCM).…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, W.Pedrycz et al presented some knowledge-based clustering methods, including context fuzzy C-means method (CFCM). It is also considered as a strong aid of rule extraction and data mining from a set of data [1,2,[4][5][6][7][8], in which fuzzy factors are really common and rise up various trends to work on.…”

Section: Introductionmentioning

confidence: 99%

Some context fuzzy clustering methods for classification problems

Cường

Sơn

Chau

2010

Proceedings of the 2010 Symposium on Information and Communication Technology - SoICT '10

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In this paper, we will propose a two-context fuzzy clustering algorithm (2C-FCM) and its parallel solution so called P2C-FCM for the classification problems. Some initial experiments show the effectiveness of P2C-FCM and 2C-FCM when comparing with traditional Context FCM. The applications of P2C-FCM and 2C-FCM are the basis to generate fuzzy rules for classifying member countries of United Nation Organization (UNO) according to the Human Development Index based on the statistics of UNO in 2005.

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“…Often, fuzzy logic is used in such applications because of its ability to describe (linear) local function approximations, as shown by [2]. Since many types of neural networks share the same property, it is both common practice [9] to extract rules from nets and to use nets for predicate calculus evaluation [10], [11].…”

Section: Related and Future Workmentioning

confidence: 99%

AI Techniques for Waste Water Treatment Plant Control Case Study: Denitrification in a Pilot-Scale SBR

Sottara

Luccarini

Mello

Lecture Notes in Computer Science

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We propose to show how different AI techniques might be used in the development of a modular expert system, acting as a manager and advisor for the operation of a pilot-scale SBR urban wastewater treatment plant, fed with real sewage. The plant's depurative effectiveness and global biomass' health depend on the reactions of nitrification and denitrification, with the former taking place as soon as the latter is complete. Since the duration of the reaction cannot be predicted, we have trained an intelligent software to recognize the event analyzing the profiles of some available signals, namely pH, orp and dissolved oxygen, thus allowing us to optimize the process' yield and detect possible failures. Using a SOM neural network, the system has been trained to remember an adequate set of reference signals, which have been given meaning using Bayesian belief techniques. Eventually, using the formalism provided by logical languages, reasoning capabilities have been imparted to the system, allowing the real-time, online deduction of new pieces of needed information. Thanks to the integration of these techniques the system is able to assess the status of the plant and act according to the adequate known policies.

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The potential of fuzzy neural networks in the realization of approximate reasoning engines

Cited by 16 publications

References 42 publications

1.2-mW Online Learning Mixed-Mode Intelligent Inference Engine for Low-Power Real-Time Object Recognition Processor

1.2-mW Online Learning Mixed-Mode Intelligent Inference Engine for Low-Power Real-Time Object Recognition Processor

Some context fuzzy clustering methods for classification problems

AI Techniques for Waste Water Treatment Plant Control Case Study: Denitrification in a Pilot-Scale SBR

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