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In the recent years, error recovery circuits in optimized data path units are adopted with approximate computing methodology. In this paper the novel multipliers have effective utilization in the newly proposed two different 4:2 approximate compressors that generate Error free Sum (ES) and Error free Carry (EC). Proposed ES and Proposed EC in 4:2 compressors are used for performing Partial Product (PP) compression. The structural arrangement utilizes Dadda structure based PP. Due to the regularity of PP arrangement Dadda multiplier is chosen for compressor implementation that favors easy standard cell ASIC design. In this, the proposed compression idealogy are more effective in the smallest n columns, and the accurate compressor in the remaining most significant columns. This limits the error in the multiplier output to be not more than 2 n for an n X n multiplication. The choice among the proposed compressors is decided based on the significance of the sum and carry signals on the multiplier result. As an enhancement to the proposed multiplier, we introduce two Area Efficient (AE) variants viz., Proposed-AE (P-AE), and P-AE with Error Recovery (P-AEER). The proposed basic P-AE, and P-AEER designs exhibit 46.7%, 52.9%, and 52.7% PDP reduction respectively when compared to an approximate multiplier of minimal error type and are designed with 90nm ASIC technology. The proposed design and their performance validation are done by using Cadence Encounter. The performance evaluations are carried out using cadence encounter with 90nm ASIC technology. The proposed-basic P-AEA and P-AEER designs demonstrate 46.7%, 52.9% and 52.7% PDP reduction compared to the minimal error approximate multiplier. The proposed multiplier is implemented in digital image processing which revealed 0.9810 Structural SIMilarity Index (SSIM), to the least, and less than 3% deviation in ECG signal processing application.

This paper describes LSE method for improving Takagi-Sugeno neuro-fuzzy model for a multi-input and multi-output system using a set of data (Mackey-Glass chaotic time series). The performance of the generated model is verified using certain set of validation / test data. The LSE method is used to compute the consequent parameters of Takagi-Sugeno neurofuzzy model while mean and variance of Gaussian Membership Functions are initially set at certain values and will be updated using Back Propagation Algorithm. The simulation using Matlab shows that the developed neuro-fuzzy model is capable of forecasting the future values of the chaotic time series and adaptively reduces the amount of error during its training and validation.

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